Graph Neural Networks for SEO: Enhancing Link Structure – Next Gen with Hyper-Intelligence

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This project is designed to help improve a website’s internal linking structure, which is a crucial factor in Search Engine Optimization (SEO). Internal links are the connections between different pages on the same website, and search engines like Google use these links to understand how your content is related. A well-organized link structure can help search engines determine which pages are important, making your content more likely to rank higher in search results.

1. Why Is This Important?

When search engines analyze a website, they look at how well the pages are linked. Search engines might struggle to find and rank important pages if a website has a messy or unclear link structure. Your website might not perform well in search results, even if you have valuable content. This project helps website owners fix this problem by suggesting ways to improve the connections between their web pages.

2. How Does This Project Work?

The project uses Graph Neural Networks (GNNs), a machine learning model, to analyze and improve the internal linking structure. A graph in this context is a way to represent your website’s pages (nodes) and the links between them (edges). Think of it as a map of your website, showing how everything is connected. The GNN processes this map and learns patterns that make a good linking structure. Based on what it learns, the GNN can suggest better ways to link your pages together, making it easier for search engines to understand and rank them.

3. Why Use GNNs for This?

Graph Neural Networks are especially powerful for this task because websites are naturally structured like a network of connected pages. GNNs excel at analyzing complex relationships in networks. By applying GNNs, the project can make intelligent, data-driven recommendations for improving internal links in a way that manual methods or traditional SEO tools might miss.

What are Graph Neural Networks (GNNs)?

Graph Neural Networks (GNNs) are a type of machine learning model that works well with data structured in graphs. In a graph, things like pages, users, or keywords can be represented as nodes, and relationships between them (like links between pages or user clicks) are represented as edges. GNNs help analyze and learn from this kind of data.

How do GNNs apply to SEO?

In SEO (Search Engine Optimization), GNNs can be used to understand and model complex relationships between different elements like:

1. Content: How pieces of content on a website are related.
2. User behavior: How users interact with the website, including which pages they visit, how long they stay, and where they click.
3. Link structures: The internal and external links between pages.

A GNN can analyze these relationships to optimize the website, making it easier for search engines like Google to understand its content and relevance to user queries. This can boost the website’s ranking on search results.

Use Case for a Website

Let’s imagine you own a website with hundreds of pages. Each page has text content, links to other pages, and users who visit and interact with the site. A GNN model can do the following:

• Content Optimization: GNNs can analyze the structure of your content and suggest how to connect different pages through internal links to make the website more discoverable. For example, if your homepage talks about a product and there are detailed blog posts on the same topic, the GNN could identify these relationships and recommend linking them so that search engines understand the content hierarchy.
• User Behavior: By studying how users move through your site, GNNs can predict which pages are most valuable to users. This information can help you focus on improving those pages or creating content similar to the pages that perform well.
• Link Structure Optimization: GNNs can model the internal and external links between pages and suggest the best ways to structure links to boost SEO performance. For example, it could be recommended that certain high-traffic pages link to less popular but valuable content, improving the SEO ranking for those pages.

What kind of data is needed for GNN in SEO?

1. URLs and Website Data: Yes, the model needs the actual URLs of the pages from your website. GNN models require the content (text, metadata) from these pages. This content can be automatically fetched from the URLs, or you can provide it in a CSV file if you have a structured format.
   • Text Content: GNN models analyze the text content from your web pages to understand what each page is about.
   • Links (Internal & External): The GNN needs to know how pages are linked. For this, it looks at internal and external links on each page.
   • User Behavior Data: This is helpful but optional if you have user interaction data (like which pages users visit the most or click-through rates). It can help refine the model’s understanding of what users find valuable.
2. CSV Format: You can also provide this data in CSV format, which is a simple table that includes things like page titles, URLs, links between pages, and metadata. For example:
   • Page URL: The web address.
   • Title/Keywords: What is each page about?
   • Internal/External Links: What other pages or websites does a page link to?
   • User Metrics: Information like page views or bounce rates (optional).

Real-Life Implementation Example

Suppose you’re managing an e-commerce website. A GNN can help in the following ways:

• It can model the relationship between your product pages, blog content, and external links and suggest the most relevant internal linking strategies to boost SEO.
• It can analyze user behavior to determine which products users are most interested in and optimize the links and content around those products to improve search engine rankings.
• If you have a blog, GNN can recommend how to interlink relevant blog posts and product pages to improve the overall site authority in search engines’ eyes.

How Does the GNN Code Work?

• The GNN model will take your website URLs and user data (if available), process the relationships between the different pages (using the internal and external links), and predict how to optimize the site structure.
• The model will likely need to preprocess the content from these URLs (extracting text, links, metadata) or from CSV files if that’s how the data is provided.
• After running the model, it will provide an output, suggesting how to organize your site, which pages to interlink, and what changes may improve user experience and SEO rankings.

What Are the Benefits for Website Owners?

For website owners, this project aims to increase their website’s visibility on search engines like Google. With better internal linking:

• Search engines will find important pages more easily, helping them rank higher in search results.
• Users will navigate the site better, improving their experience by moving between relevant pages without getting lost.
• SEO performance will improve overall, leading to more traffic, better engagement, and potentially more conversions (like sales or sign-ups).

What Does This Project Produce?

The project’s output is a list of recommendations that tell website owners exactly which pages should be linked together to enhance SEO. These recommendations are based on a detailed analysis of the site’s structure and the relationships between pages. For example, the model might suggest linking a services page to a related blog post or a category page to a detailed product page. Following these recommendations, website owners can take actionable steps to optimize their site’s internal linking and boost SEO.

How Can This Be Implemented?

Once the GNN provides its recommendations, the website owner or their SEO team can manually create the links between the suggested pages. This process can be done through the website’s content management system (CMS) or by editing the site’s code. The implementation is straightforward, and the results can significantly improve the site’s search rankings.

1. import networkx as nx

·         Purpose: This line imports the NetworkX library, which is used to create and manipulate graphs. In the context of this project, a graph represents your website. Each page is a node, and the links between pages are called edges. NetworkX helps build and analyze this graph.

·         Why it’s used: We need a way to represent the relationship between web pages on your website, and graphs are perfect for showing how pages link.

2. import requests

·         Purpose: This line imports the Requests library, which sends HTTP requests to websites. It lets the program visit a web page and download its content.

·         Why it’s used: We need to gather information from the website’s pages (like the text and the links), and Requests is how we fetch the pages’ content from the internet.

3. from bs4 import BeautifulSoup

·         Purpose: This line imports BeautifulSoup, a tool for parsing HTML (the code structure of web pages). It helps extract specific information, like the links and text on a page, by making it easier to navigate and search through the web page’s structure.

·         Why it’s used: Once we download a page using requests, we need to extract the links from that page, and BeautifulSoup helps us do that by breaking down the HTML code in a simple, readable format.

4. import torch

·         Purpose: This imports PyTorch, a popular machine learning library. PyTorch is used to build and train machine learning models, including Graph Neural Networks (GNNs), which are the focus of this project.

·         Why it’s used: PyTorch provides the tools to create and train a GNN model to analyze the website’s structure and recommend ways to improve internal links.

5. import torch.nn as nn

·         Purpose: This imports the neural network module from PyTorch. A neural network is a machine learning model miming how the brain processes information. In this case, it is used to create the layers of the Graph Neural Network.

·         Why it’s used: To build the GNN, we need to define different layers of the network (like in a neural network). torch.nn helps create those layers.

6. import numpy as np

·         Purpose: This imports NumPy, a library for numerical computing. It’s used to work with arrays and matrices, essential for performing mathematical operations in machine learning.

·         Why it’s used: Machine learning models, especially neural networks, need to handle a lot of numbers (like weights, inputs, and outputs). NumPy helps perform these calculations efficiently and organize data into formats (like arrays and matrices) that the model can use.

Step-by-Step Explanation

1.    Defining the URLs:

• What this does: We create a list called URLs that contains the addresses of different pages on your website. We will analyze this list.
• Output: Nothing yet. It’s just setting up the data that the program will use later.

2. Defining the extract_links function:

• What this does: This function will take a URL, download the webpage content, and extract all the links (both internal and external) from that page.
• Output: It will return a set (a collection) of links on the page. If there is an error, it will return an empty set and print an error message.

3. Running the function:

• What this does: The program will loop over the URLs we defined earlier and run the extract_links function on each one. It will print out all the links found on each page.
• Output: When you run this, you will see a list of links on each webpage. For example:

What Happens in Each Step:

• Step 1.1: The program requests the website and gets the HTML of the webpage (similar to viewing the source code in your browser).
• Step 1.2: The HTML is parsed into a structured format so the program can easily search for elements (in this case, <a> tags that contain links).
• Step 1.3: The program stores the links in a set. Sets automatically remove duplicates, so we don’t end up with the same link multiple times.
• Step 1.4 – 1.5: The program looks through all the <a> tags on the page and checks if they contain valid links (starting with http or /). These are the links we want to keep.
• Step 1.6: Each valid link is added to the set.
• Step 1.7: Finally, the function returns all the links found on the page.

Step-by-Step Explanation

1.    Function Definition (build_graph):

• What this does: The function build_graph creates a graph of your website. Each page (URL) will be a node, and the links between the pages will be edges. This graph will represent the structure of your website, specifically how the pages are interconnected.

2.    Creating an Empty Graph (G = nx.Graph()):

• What this does: It initializes an empty graph. A graph is essentially a network of connected points. In this case, the points are the URLs (webpages), and the connections (edges) are the links between them.
• Output: At this point, no output is generated. The graph is empty, waiting for nodes and edges to be added.

3.    Looping through URLs:

• What this does: The function adds that page to the graph as a node for each URL in the list (the list contains the pages on your website). Each webpage is considered a node in the network.
• Output: When this loop runs, nodes representing each webpage will be added to the graph. However, this won’t produce any visual output just yet, as the structure is still being built internally.

4.    Extracting Links from Each Page (links = extract_links(url)):

• What this does: For each URL, the function extract_links is called, which fetches all the links on that page. This step gathers the relationships between the pages (i.e., which pages link to which others).
• Output: The output here is a list of links found on the webpage. For example, if you run this on the homepage, it will return a set of links like:

5.    Filtering Internal Links:

• What this does: The code checks whether each link is internal or external. Internal links point to other pages within your website (starting with ‘https://thatware.co’ or ‘/’).
• Output: Links that point to other websites will be ignored, and only internal links will be considered.

6.    Adding Edges to the Graph:

• What this does: The function adds an edge to the graph for each internal link found on the page. An edge represents a direct connection between two pages (i.e., one page links to another). For example, if the homepage links to the “Services” page, an edge will be added between these two nodes in the graph.
• Output: Internally, the graph will now represent the structure of your website by showing how different pages are connected.

7.    Returning the Graph:

• What this does: After all nodes and edges have been added, the function returns the complete graph. This graph can then be used for further analysis, such as visualizing the structure or identifying areas for optimization.
• Output: The complete graph structure representing your website. However, you will see this visually later. It will be stored as a variable you can visualize or analyze later.

Expected Output When You Run It:

When you run this code, any immediate output won’t be visible on your screen because you’re building a data structure (the graph) behind the scenes. The graph contains nodes (your URLs) and edges (the links between them), and you can later visualize it or use it for SEO analysis.

Here’s what happens after running:

1.    Graph structure built: The graph will have nodes representing each page on your website and edges representing the internal links between these pages.

2.    Example Graph: If your website’s homepage has links to the “Services” page and the “Contact Us” page, the graph would look like this (conceptually):

These relationships are stored in the G object and can be visualized later.

How to Visualize the Graph (Optional):

After building the graph, you can visualize it using the following code to get a visual understanding of your website’s link structure:

• Expected Output: This will show a visual diagram of your website, with each page being a node and each link connecting the nodes.

Detailed Step-by-Step Explanation

1.    What is this step about?

• Purpose: This step focuses on understanding the structure of the website. It helps us visualize how the pages (nodes) are linked (edges), which is crucial for SEO because internal linking improves the navigation and discoverability of content on your website.

2.    Step 3.1: Printing the Nodes (Pages)

• What this does: The G.nodes() function gives us a list of all the nodes in the graph, where each node represents a page on your website. In simple terms, it prints out all the URLs (pages) added to the graph.
• Example Output: This is what you’ll see when the function runs:

• Why it’s important: It helps you see all the pages added to the graph, so you know which pages are being considered in the link structure.

2. Step 3.2: Printing the Edges (Links)

• What this does: The G.edges() function returns a list of all the edges in the graph, where each edge represents a link between two pages (nodes). It shows you which pages are connected by internal links.
• Example Output: You will get a list of links between pages like this:

• Why it’s important: This shows the relationships between pages in your website’s internal link structure. By visualizing this, you can understand how well your pages are connected or if certain pages are isolated (which could hurt SEO).

4.Step 3.3: Visualizing the Graph

o What this does:

• The plt.figure(figsize=(10, 8)) sets the size of the visual graph.
• The nx.draw(G, with_labels=True, …) command draws the graph using the networkx library and customizes the appearance (node size, color, font size, etc.).
• The plt.show() function displays the graph.

o What it shows:

• Nodes (web pages) appear as circles.
• Edges (internal links) appear as lines connecting the nodes.

o Why it’s important: Visualizing the graph is an excellent way to understand your website’s link structure. You’ll be able to visually see how well-connected your pages are and identify any isolated pages (those without internal links) that might need improvement.

Example of What the Visual Output Would Look Like

When you run this visualization code, a window will pop up showing the graph structure of your website. It will look something like this (imagine a diagram where circles represent pages, and lines between the circles are the links between those pages):

·         Nodes (Circles): Represent the pages on your website, such as:

• https://thatware.co/
• https://thatware.co/services/
• https://thatware.co/advanced-seo-services/

·         Edges (Lines): Represent the links between these pages, showing how pages are connected.

How to Use the Output:

·         Nodes List: After running the visualize_graph function, you’ll first see a list of all the pages in your website’s structure. These are the nodes of the graph.

·         Edges List: Next, you’ll see a list of links (edges) between those pages. This is how the pages are linked together.

·         Graph Visualization: Finally, you’ll get a graphical view of how your website’s internal links are structured. This is extremely useful because you can visually see your internal links’ strength and connectivity and identify gaps where links should be added.

Overview

This code defines a Graph Neural Network (GNN) model that can be used to learn relationships between different web pages by analyzing their links. It’s like teaching the computer to “see” how pages are connected and using that information to improve internal linking for SEO (Search Engine Optimization).

Step 4: Define the GNN Model

Explanation:

• What this step does: This defines the core part of the Graph Neural Network (GNN), which processes data (like the connections between pages) to learn patterns. In simple terms, this is the “brain” of the network.
• Parameters:
  • input_dim: The number of features for each page (e.g., attributes like page rank, type of content, etc.).
  • hidden_dim: The size of the “hidden layer” — it allows the model to learn more complex patterns.
  • output_dim: The size of the final output, representing the learned data from the GNN (you could use this to classify or recommend things).
• Use case: You use this model to analyze how web pages on your website are connected and process that connection to find meaningful insights (e.g., which pages are the most influential).

Example:

• Think of this as a machine that processes a series of connected pages, each with information about itself, and transforms that information into something more useful by learning from the relationships.

Step 5: Prepare Dummy Data for Testing the GNN

Explanation:

• What this step does: This creates some random “dummy” data so that you can test your GNN model. This data represents the pages of your website (called “nodes”) and the links between them (called “adjacency matrix”).
  • features: This is a matrix where each row represents a page, and each column represents some characteristic (called a “feature”) of that page. For example, one feature could be the length of the content, another could be the number of incoming links, etc. Here, it’s randomly generated data.
  • adj_matrix: This is an “adjacency matrix” that describes which pages are linked to which. In the simple case, we use an identity matrix, meaning each page is linked only to itself. In a real case, this matrix would represent the internal links between pages on your website.

Use case:

• This part of the code helps you simulate and test the GNN model with fake data before applying it to real data. When you’re ready to use the model, you’d replace this fake data with real data from your website.

Example:

• If your website had 3 pages, this would generate 3 random features for each page and create a matrix that assumes each page is only linked to itself. It’s like setting up a test environment before running the real analysis.

Step 6: Run the Model and Display the Output

Explanation:

• What this step does: This is where you run the model using the dummy data you created earlier. You pass the feature matrix and the adjacency matrix into the model, and the model processes it to produce an output.
  • input_dim=3: Each page has 3 features (as we defined in Step 5).
  • hidden_dim=4: The model will learn using a hidden layer with 4 features.
  • output_dim=2: After the model processes the data, it will output 2 final features for each page.
• Use case: The model processes the relationships between the pages and their features and gives back some meaningful output. In real-world use, this output could tell you which pages are most important, how you can optimize your internal links, etc.

Output:

• The output is the final result of the GNN model for each page on your website. After processing the connections and features, this output shows how the model “sees” each page.

Example Output:

What the Output Means:

• Rows: Each row in the output corresponds to a page (node) on your website.
• Columns: Each column represents a feature that the model learned. In this case, after processing the 3 input features for each page, the model outputs 2 new features per page. These could be used to decide which pages are more important or how to optimize internal links.

Absolutely! I will rewrite the code with detailed comments and explanations for each step so that anyone without a technical background can easily understand what’s happening at each part of the code. Let’s break it down:

Detailed Explanation of Each Step:

1.    Step 5.1: Count the number of pages (nodes)

• Explanation:
  • This line retrieves the number of pages in your website’s graph. Every page is considered a “node” in the graph.
  • We need to know how many pages we have to build the feature and adjacency matrices correctly.
• Output:
  • If your graph has 10 pages, num_nodes will be 10.

2.    Step 5.2: Create a Feature Matrix

• Explanation:
  • This step creates a feature matrix where each row corresponds to a webpage (node). Since we’re using “one-hot encoding,” each page is represented by a vector where one element is set to 1 and all others are set to 0. This means every page has a unique representation.
  • It’s a simple way of saying, “This row is for page 1, this row is for page 2, etc.”
• Use Case:
  • This is how we give each page its own identity in the GNN. Later, we can add more features to each page, but we give them simple, unique vectors for now.
• Example Output (for 3 pages):

o In this example, the first row represents Page 1, the second row represents Page 2, and the third row represents Page 3.

3. Step 5.3: Create the Adjacency Matrix

• Explanation:
  • The adjacency matrix represents the website’s structure, showing which pages link to each other. If page i links to page j, then the corresponding cell in the matrix is set to 1. Otherwise, it’s 0.
  • This matrix is very important because it tells the GNN how the pages are connected. The GNN uses this information to learn from the graph structure.
• Use Case:
  • The adjacency matrix helps the GNN understand the link relationships between different pages on the website.
• Example Output (for a simple website with 3 pages where Page 1 links to Page 2)

Step 5.4: Convert the Adjacency Matrix into a PyTorch Tensor

• Explanation:
  • Machine learning models in PyTorch require input data as “tensors” (multidimensional arrays). Here, we are converting the adjacency matrix from its NumPy format (a common data structure in Python) to a PyTorch tensor, a format that can be used in GNN training.
• Use Case:
  • This step ensures that the GNN can use the adjacency matrix to learn about the page links.
• Example Output:

1.  

o     

• This output looks the same as the adjacency matrix but is now in a format (tensor) that PyTorch can understand.

2.    Step 5.5: Return the Prepared Data

• Explanation:
  • This step returns the feature matrix and adjacency matrix once they are ready. These are the two key pieces of data that the GNN model will use to learn about the website’s structure.
• Use Case:
  • The GNN model needs these matrices to start its training process. The feature matrix tells the model about each page, and the adjacency matrix tells it how pages are connected.

Full Explanation in Simple Terms:

·         What’s happening:

• You’re taking your website (with pages and links between them) and transforming it into two types of data:
  1. A feature matrix that represents each page with a unique vector.
  2. An adjacency matrix that represents the links between pages.
• These two matrices are like a map of your website’s structure, and the GNN model uses them to learn how pages are related.

·         Why this is important:

• The GNN model needs these matrices to “understand” the structure of your website. It will use this information to suggest improvements to your internal links or highlight important pages. By preparing this data, you’re setting up the model to analyze your website effectively.

·         Outputs:

• If you print the outputs of the feature and adjacency matrices after running this function, you will see how the pages are represented and connected.

Detailed Explanation of the Code (Step-by-Step):

1. Function Definition (train_gnn(G)):

• What it does: This function is designed to train a Graph Neural Network (GNN) using the structure of your website, represented as a graph. The GNN aims to predict an optimized version of the internal linking structure.
• Use case: As a website owner, optimizing internal links can improve how search engines crawl and understand your site, ultimately boosting SEO performance.

2. Step 6.1: Prepare the feature and adjacency matrix:

• What it does:
  • The prepare_gnn_data(G) function prepares two things:
    1. node_features: A matrix where each webpage (node) is represented by a feature (in this case, a one-hot encoded vector).
    2. adj_matrix: A matrix that represents how the nodes (pages) are linked. Each value indicates whether a link exists between two pages.
• Why it’s important:
  • The GNN requires data to understand which pages are on the website (nodes) and how they are currently linked (edges). This input helps the model learn about the site’s structure.
• Example:
  • If your website has 3 pages, the node_features matrix will look like this:

The adj_matrix shows the links between pages, e.g.:

3. Step 6.2: Initialize the GNN model:

• What it does:
  • This initializes the GNN model using the GNNModel class we defined earlier. We specify the number of input features (input_dim), hidden features (hidden_dim), and output features (output_dim).
• Why it’s important:
  • The GNN needs to learn relationships between pages (nodes). It will process the input feature matrix and predict an updated (optimized) link structure for the website.
• Example:
  • If each page has 3 features and we have 5 pages, input_dim = 3, output_dim = 5. The hidden dimension (16) allows the model to learn complex relationships between the pages.

4. Step 6.3: Define the loss function and optimizer:

• What it does:
  • criterion: This is the loss function. We use Mean Squared Error (MSE) to measure how far the predicted adjacency matrix (link structure) is from the actual one.
  • optimizer: The Adam optimizer adjusts the model’s internal parameters (weights) during training to minimize the loss.
• Why it’s important:
  • The loss function gives the model feedback about its performance. The optimizer adjusts the model’s parameters to reduce this error during training.
• Example:
  • Suppose the predicted adjacency matrix says that Page 1 links to Page 2 with a probability of 0.7, but the actual value is 1. In that case, the loss measures how far 0.7 is from 1 and penalizes the model accordingly.

5. Step 6.4: Training loop:

• What it does:
  • The training loop runs for 100 iterations (epochs). Each epoch trains the model and updates the parameters.
• Why it’s important:
  • The model needs to go through multiple cycles to learn how to improve. Each cycle adjusts the model’s understanding of the website’s structure, gradually improving its ability to predict an optimized internal link structure.

6. Step 6.5: Forward pass through the GNN:

• What it does:
  • This is the forward pass of the model. The GNN takes in the feature matrix (node_features) and the adjacency matrix (adj_matrix), and predicts an updated adjacency matrix (the new link structure).
• Why it’s important:
  • This step is crucial because the model uses the input to predict. It’s like the model thinking: “Given these pages and their current links, what should the new link structure be?”
• Example:
  • If your website has 3 pages, the output matrix could look like:

·         These values indicate the probability of links between the pages.

7. Step 6.6: Calculate the loss:

• What it does:
  • The loss function compares the predicted adjacency matrix (output) to the actual one (adj_matrix). The difference between the two is the “loss” or error.
• Why it’s important:
  • The model needs to know how far off it is. The loss function provides feedback to the model, allowing it to adjust its parameters and improve future predictions.

8. Step 6.7: Backpropagation:

• What it does:
  • This step computes the gradients of the loss concerning the model’s parameters. It tells the model which parameters need to be adjusted and by how much.
• Why it’s important:
  • The GNN learns by adjusting its internal parameters. The gradients guide these adjustments, helping the model learn the most important links.

9. Step 6.8: Update the model’s weights:

• What it does:
  • The optimizer updates the model’s parameters (weights) based on the gradients calculated in the previous step.
• Why it’s important:
  • By adjusting the weights, the model improves its prediction of the optimized link structure. The predictions should become more accurate with each iteration.

10. Step 6.9: Print the loss:

• What it does:
  • Every 10 epochs, we print the loss to track how much the model is improving.
• Why it’s important:
  • Seeing the loss decrease over time lets you know that the model is learning. A lower loss means the predicted link structure is becoming more accurate.

11. Step 6.10: Return the final output:

• What it does:
  • After complete training, we return the predicted adjacency matrix (output) and the actual adjacency matrix (adj_matrix) for comparison.
• Why it’s important:
  • This allows you to see how well the model performed and what it thinks the optimized link structure should be.
• Example Output:
  • The output will show the predicted links between pages and allow you to compare them with the original structure.

Example Output:

• Loss values: These tell you how much the model’s

Nodes (Pages):

·         What are nodes?

• In graph theory, nodes are individual entities or points. In the case of a website, each node represents a specific page on your website.
• Each URL listed in the “Nodes” section is a webpage on your site. For example:
  • https://thatware.co/
  • https://thatware.co/ai-implementations-seo/
  • https://thatware.co/google-penalty-recovery/
    These are individual pages on your website. Each page is a “node” in the website’s graph structure.

·         Why are nodes important in SEO?

• Nodes (pages) are crucial for SEO because each page of your website can contribute to how well the website ranks in search engines. By examining each node, you can assess your content is well-structured, identify isolated pages, and understand the internal linking strategy.

2. Edges (Links):

·         What are edges?

• Edges represent connections between nodes; in a website’s graph, these connections are links between pages. An edge is a line connecting two nodes, representing that one page links to another.
• Each edge shows that one webpage (node) links to another. For example:
  • (‘https://thatware.co/’, ‘https://thatware.co/ai-implementations-seo/’)
    • This means the page https://thatware.co/ has a link to the page https://thatware.co/ai-implementations-seo/.
  • (‘https://thatware.co/’, ‘https://thatware.co/google-penalty-recovery/’)
    • This means the page https://thatware.co/ has a link to the page https://thatware.co/google-penalty-recovery/.

·         Why are edges important in SEO?

• Links (edges) between pages help search engines understand the relationship between the content on different pages. Internal links help distribute “link equity” or SEO value across your site, improving the rankings of linked pages. Well-structured internal links make it easier for search engines to crawl and index your website.
• If a page has many high-quality links, it may rank better because search engines see it as more important. Similarly, search engines use internal links to understand the most important content on your website.

Understanding Nodes and Edges in SEO: Importance and Use Case

In the output, nodes and edges represent the website’s internal linking structure. Let’s break it down in simple terms:

What Are Nodes?

• Nodes in this context represent webpages on your website. Each node is a different URL or webpage on your site.
  • Example: https://thatware.co/ is a node representing the homepage, and https://thatware.co/seo-services-portsmouth/ is a node representing the Portsmouth SEO Services page.

What Are Edges?

• Edges are connections between nodes, or in simpler terms, links between webpages. If one webpage links to another, there’s an edge between those two nodes.
  • Example: If your homepage https://thatware.co/ links to https://thatware.co/seo-services-portsmouth/, an edge (or connection) exists between these two nodes.

Why Are Nodes and Edges Important in SEO?

1. Internal Linking Improves Navigation:

• Benefit for Users: Well-connected nodes (webpages) through internal links (edges) make it easier for users to navigate your site. If a user lands on the Portsmouth SEO Services page with links (edges) to other relevant services or the homepage, they can easily move around without leaving your website.
• Example: If a user is reading about Healthcare SEO Services and they see a link to Advanced SEO Services, they might click on it and spend more time exploring your services, which increases engagement and leads to more conversions.

2. Search Engine Crawling and Indexing:

• Benefit for Search Engines: Search engines like Google use web crawlers to explore your website. These crawlers navigate from one page (node) to another via internal links (edges). If your pages are well connected, crawlers can easily discover all of your pages, ensuring that nothing gets left out of the index.
• Example: If your SEO Services in Brighton page is well-linked from other parts of your site, Google’s crawlers will recognize it as an important page, which could help improve its ranking.

3. Passing Link Authority (PageRank):

• Benefit for SEO: Links between nodes (pages) help to pass link authority. Pages that have a lot of internal links pointing to them (edges) are seen as more important in the eyes of search engines. This can help boost the rankings of those important pages.
• Example: If many of your pages link to your Contact Us page, Google will consider that page important. This can increase the chances of that page ranking higher when someone searches for contact-related queries for your business.

4. Reducing Bounce Rate and Improving Engagement:

• Benefit for User Experience: If you have strong internal linking, users are less likely to bounce (leave your site after visiting just one page). They are more likely to click on internal links to explore related services, blog posts, or other parts of the website.
• Example: If a user lands on your Google Penalty Recovery page but then sees a link to Advanced SEO Services, they may continue browsing, improving the session time on your site, which can indirectly help with SEO rankings.

How to Explain This to Your Client

When explaining this to your client, you want to emphasize why internal linking (creating edges between nodes) is crucial for the success of the website:

1.    Nodes Represent Pages: Every page on your website (node) is a potential point of entry for both users and search engines. It’s important that these pages are linked to each other effectively.

2.    Edges (Links) Connect Pages: Links (edges) between these pages help users easily move from one part of the website to another and help search engines crawl the website more efficiently.

3.    SEO Benefits: Internal linking improves search engine rankings by:

• Allowing search engines to discover all important pages.
• Distributing the link authority or PageRank across various pages.
• Helping Google understand the hierarchy and importance of pages on your site.

4.    User Experience Benefits: A well-connected website encourages users to explore more, which leads to longer session durations, reduced bounce rates, and more potential conversions (users becoming customers).

Example Use Case for Internal Linking

Let’s say you have a visitor landing directly on your Healthcare SEO Services page (https://thatware.co/healthcare-seo-services/). This visitor may not have come through the homepage but instead arrived from a search engine or external link. Now, if this page includes internal links (edges) to the homepage and other related services, it:

• Keeps the visitor on your site longer (since they may click other links).
• Helps the visitor discover more of your services that they didn’t know existed.
• Shows Google that these links are relevant and connected, boosting SEO for both pages.

For instance, linking Healthcare SEO Services to Advanced SEO Services ensures that Google understands the relationship between these services. It can also lead to higher rankings for both pages because they are interconnected.

Why Internal Linking Improves SEO and Traffic

• Boosts Page Authority: Pages that are internally linked to other important pages carry more weight in Google’s eyes.
• Encourages Discovery: Google favors sites that make it easy for crawlers and users to move around, which enhances your SEO.
• Improves Ranking for Target Keywords: By linking certain pages with the right anchor text (the clickable part of the link), you signal to Google what the page is about, helping it rank for those specific keywords.

Final Conclusion for Your Client

After receiving the output from the model, it’s essential to implement the suggested internal links because:

1. It improves SEO rankings by helping search engines like Google better understand and crawl your site.
2. It enhances user experience, making it easier for visitors to discover more content and services on your site.
3. It reduces bounce rates and increases session time, which are factors that indirectly influence Google rankings.
4. It helps spread link authority across important pages, boosting the visibility of those pages.

1. Linking Internal Pages to the Homepage (https://thatware.co/)

·         Example: Consider linking https://thatware.co/healthcare-seo-services/ to https://thatware.co/

Importance of this Link:

• User Flow: It is not always guaranteed that a user will visit your homepage first and then navigate to other pages. Users may land directly on a service page like “Healthcare SEO Services” through a search engine or external link.
• Navigation: By linking back to the homepage from service pages, you ensure that users can easily return to the main part of the site. This enhances user experience by making the site easier to navigate.
• SEO Impact: Search engines like Google use internal links to understand the relationship between different pages on your website. By linking to the homepage, you’re signaling to Google that this is a core, highly important page, which can improve the ranking of your homepage.

Conclusion: This link promotes user navigation and makes it easier for search engines to crawl your website’s important pages.

2. Linking Service Pages to Other Core Pages (https://thatware.co/services/)

·         Example: Consider linking https://thatware.co/reinforcement-learning-enhanced-seo/ to https://thatware.co/services/

Importance of this Link:

• Relevance: Linking specific service pages to the “Services” page is important because it establishes a clear relationship between individual services and the broader portfolio.
• SEO Benefit: Internal links help search engines like Google better understand the hierarchy and relevance of pages. This can help rank your “Services” page higher because Google sees that multiple pages are linking to it.
• User Navigation: Users who visit niche service pages like “Reinforcement Learning Enhanced SEO” may want to explore other services. Linking to the main “Services” page makes it easier for users to discover more options.

Conclusion: This improves the discoverability of services for users while boosting the SEO strength of the Services page.

3. Linking Between Service Pages (Cross-Linking)

·         Example: Consider linking https://thatware.co/seo-services-portsmouth/ to https://thatware.co/advanced-seo-services/

Importance of this Link:

• User Engagement: If a visitor lands on a specific service page (e.g., “SEO Services Portsmouth”), cross-linking it to other services (e.g., “Advanced SEO Services”) encourages them to explore more options. This increases engagement and time spent on the website.
• SEO Impact: By linking related pages, you’re helping Google understand that these services are interconnected. This can improve the ranking of both pages in search engine results for related queries.
• Internal PageRank Flow: Search engines like Google use PageRank to determine the importance of a page based on the number of links pointing to it. When you link related services together, you are spreading link equity, which can enhance the visibility of those pages in search engines.

Conclusion: Cross-linking between service pages strengthens your website’s overall SEO by increasing interconnectivity and promoting user engagement.

4. Linking Niche Service Pages Back to Core Services

·         Example: Consider linking https://thatware.co/seo-services-newcastle-upon-tyne/ to https://thatware.co/

Importance of this Link:

• Localized SEO: If you offer services in specific locations like Newcastle upon Tyne, linking back to your main website can help improve your local SEO. Google will recognize that these location-based service pages are part of your broader service offering, which boosts your site’s authority for local searches.
• User Experience: If someone lands on a local service page, giving them easy access to your homepage allows them to explore the entire business offering. This improves site navigation and increases the likelihood of converting a visitor into a customer.

Conclusion: This recommendation helps boost local SEO performance while improving the user’s journey through the website.

5. Linking Audits, Checklists, and Tools to Core Pages

·         Example: Consider linking https://thatware.co/seo-audit-checklist/ to https://thatware.co/services/

Importance of this Link:

• Enhances Value: If you provide an SEO audit or checklist, linking that tool to your main services page adds value for the user. A user may be interested in further services after using the audit, so this cross-promotion increases the likelihood of conversions.
• SEO Boost: SEO audit checklists are often highly targeted for SEO-related queries. Linking this content to your services helps push organic traffic from informational queries to commercial pages (where you can sell services).

Conclusion: This link directs organic traffic to service pages, improving conversion rates and providing users with additional value.

6. Linking to Contact Page for Conversions

·         Example: Consider linking https://thatware.co/advanced-seo-services/ to https://thatware.co/contact-us/

Importance of this Link:

• Conversion Optimization: A contact page is crucial for converting users into customers. If someone is on your Advanced SEO Services page, providing a direct link to the Contact Us page can encourage them to inquire about your services, leading to more leads and sales.
• User Path: For service-based businesses, making the next step clear is essential. If a potential client is exploring your services, the Contact Us page should be easily accessible for them to get in touch.

Conclusion: This is a crucial link for lead generation and increasing conversions from service pages.

How Internal Linking Affects Google Ranking and SEO:

1.    Improved Crawlability: Internal links help search engines like Google discover and index pages on your site. If a page is not linked internally, it might be hard for Google to find it.

2.    Link Authority Distribution: When you link internally, you pass PageRank (authority) from one page to another. This helps boost the ranking of important pages like service pages, the homepage, or your contact page.

3.    Keyword Relevance: Internal links can improve how Google understands the content of your pages. Linking a keyword-rich page to a service page tells Google that the linked page is relevant to that specific keyword or topic.

4.    Reduced Bounce Rate: Internal links encourage users to explore more of your site, which reduces bounce rate (users leaving after viewing one page). Lower bounce rates often correlate with better rankings.

5.    Enhanced User Experience: Making it easy for users to navigate between pages improves their experience, leading to longer time spent on the site and a higher likelihood of converting visitors into customers.

Understanding the Output:

Epoch Output (Training Information):

This output is the GNN model’s training log, which shows how well the model is learning to understand your website’s link structure based on the relationships between the pages and their links.

Let’s break it down:

Explanation of Each Part of the Output:

1.    Epochs:

• The word “Epoch” refers to one complete cycle of training the model using all the data (in this case, the website structure).
• In each epoch, the model makes predictions about how the pages on your website should be linked and then adjusts itself based on the errors it makes.
• The output you see shows the model’s performance at different epochs: 0, 10, 20, 30, and so on, up to 90. For each epoch, the model calculates a loss value to measure how well it’s learning.

2.    Loss Value:

• The loss is a number that tells you how far off the model’s predictions are from the actual link structure.
• A higher loss means the model is making more mistakes, and a lower loss means it’s learning well and making fewer mistakes.
• For example, at epoch 0, the loss is 0.4769, which is quite high, meaning the model was initially not very good at understanding the link structure. However, as the training progresses, the loss drops significantly. By epoch 90, the loss is 0.0165, which is much lower, meaning the model has improved its understanding.

3.    Why is Loss Important?

• Loss is a way to measure the model’s performance. As the loss value decreases, the model becomes better at predicting how your website’s pages should be linked. This means that by the time the loss value is very low (like 0.0165), the model is likely providing good recommendations for improving your website’s internal linking and structure.

What Does This Output Mean?

• The decreasing loss shows that the model is learning the structure of your website and is becoming better at understanding how the pages are connected.
• By the end of the training (epoch 90), the model has learned enough about your website’s link structure to recommend ways to optimize the internal and external links between pages.

Recommended Internal Links to Improve SEO:

Here is the most important part to you as a website owner. The model provides specific internal linking recommendations to improve SEO.

Here’s what each line means:

·         Consider linking https://thatware.co/seo-services-brighton/ to https://thatware.co/services/

• The model has detected that the page seo-services-brighton is not well connected to the services page. By linking these two pages together, you will improve the connection between them. This is important because search engines like Google will better understand the relationship between these pages, which can help both pages rank better.

·         Consider linking https://thatware.co/healthcare-seo-services/ to https://thatware.co/

• This recommendation suggests adding a link from the healthcare-seo-services page to the homepage (https://thatware.co/). This can help users and search engines easily navigate from specific services to the main page, improving the website’s overall structure.

·         Consider linking https://thatware.co/adult-seo-services/ to https://thatware.co/

• Similarly, the model recommends linking the adult-seo-services page to the homepage for better internal linking. Google needs to see clear pathways between your main and specific service pages.

1. Consider linking https://thatware.co/seo-services-brighton/ to https://thatware.co/services/

Explanation:

• The page seo-services-brighton/ is specific to SEO services for the city of Brighton. The recommendation is to link this page to the main services page because the services page likely lists or describes all the SEO services your company offers.
• Why is this important? Linking a specific service page (Brighton SEO services) to the general services page makes it easier for users and search engines to understand how your offerings are connected. Linking the services page with related content also helps boost its authority.

Use case:

• A user looking for SEO services in Brighton can easily navigate to the broader services page to explore more of your services. Google also gets a clearer picture of how your services are organized.

2. Consider linking https://thatware.co/healthcare-seo-services/ to https://thatware.co/

Explanation:

• The page healthcare-seo-services/ is about SEO services specific to the healthcare industry. The recommendation suggests linking it back to the homepage (https://thatware.co/).
• Why is this important? The homepage is often the most authoritative page on a website. By linking important service-specific pages (like healthcare SEO) back to the homepage, you create a stronger connection between these key areas of your site. It helps users easily return to the main homepage from specific service pages and improves site navigation.

Use case:

• If a visitor is reading about healthcare SEO services but wants to explore more about your company or other offerings, they can quickly return to the homepage with this link. For Google, this shows a strong relationship between your service pages and your core website, which helps SEO.

3. Consider linking https://thatware.co/adult-seo-services/ to https://thatware.co/

Explanation:

• This recommendation is similar to the healthcare SEO services recommendation. Here, the adult-seo-services/ page is about SEO services for adult websites, and the model suggests linking it to your homepage.
• Why is this important? As with the previous example, linking specialized services (adult SEO) to the homepage strengthens the connection between your niche offerings and your main website. This improves the overall structure of your website, making it easier for users to navigate and for search engines to understand the relevance of this service.

Use case:

• A visitor reading about adult SEO services may want to explore other services or learn more about your company. By linking back to the homepage, you provide an easy way for them to do that.

4. Consider linking https://thatware.co/dating-seo-services/ to https://thatware.co/

Explanation:

• This is a recommendation for linking the dating-seo-services/ page (focused on SEO for dating websites) to the homepage.
• Why is this important? Again, this strengthens the internal connection between specialized services (dating SEO) and the homepage. It helps Google understand the relationship between your niche services and your main site, and improves user navigation.

Use case:

• For users, this makes it easier to go back to the homepage if they are interested in learning more after reading about your dating SEO services. It also helps Google see that this service is a key part of your overall offering.

5. Consider linking https://thatware.co/seo-services-oxford/ to https://thatware.co/

Explanation:

• The seo-services-oxford/ page is about SEO services specific to Oxford. This recommendation suggests linking it to the homepage to improve internal linking.
• Why is this important? Linking geographically specific service pages (like Oxford SEO) back to the homepage helps reinforce the connection between your specialized SEO services and the main content of your website. This is important for both navigation and SEO.

Use case:

• A user looking at Oxford-specific SEO services can easily go back to the homepage to explore more options or services. For Google, it helps the search engine understand that your Oxford SEO service is part of a larger business offering.

6. Consider linking https://thatware.co/seo-company-kolkata/ to https://thatware.co/

Explanation:

• The seo-company-kolkata/ page focuses on SEO services in Kolkata. The recommendation is to link this page to the homepage to strengthen internal navigation.
• Why is this important? Just like the other geographically specific service pages (Oxford, Brighton), linking the Kolkata SEO page to the homepage strengthens the connection between these city-based services and the main website. This improves how search engines understand your site and how users move between pages.

Use case:

• If a visitor is interested in SEO services for Kolkata, linking back to the homepage allows them to navigate easily to other parts of your site if needed.

7. Consider linking https://thatware.co/seo-audit-checklist/ to https://thatware.co/

Explanation:

• The seo-audit-checklist/ page is probably an important resource or guide. Linking it to the homepage will increase visibility and make it easier for users to navigate from the checklist to the homepage.
• Why is this important? By linking important resources like checklists to the homepage, you improve internal navigation and help users find their way back to key areas of your site.

Use case:

• A user reading your SEO audit checklist might want to explore more about your services or company, so providing a link to the homepage makes that easy.

8. Consider linking https://thatware.co/seo-audit-checklist/ to https://thatware.co/services/

Explanation:

• Similar to the previous point, but this time linking the seo-audit-checklist/ page to your services page.
• Why is this important? The SEO audit checklist is a valuable resource, and linking it to your services page can drive users who are interested in SEO audits to explore your paid services.

Use case:

• Users who use your checklist might also want to hire your company for more in-depth SEO services. Linking to the services page makes that easy for them.

9. Consider linking https://thatware.co/seo-companies-can-help-companies-recover-roi/ to https://thatware.co/

Explanation:

• The page seo-companies-can-help-companies-recover-roi/ likely talks about how SEO can help businesses improve their ROI (Return on Investment). Linking this back to the homepage strengthens the connection between this content and the rest of your site.
• Why is this important? Linking informative content to the homepage helps Google and users understand how this page fits into the bigger picture of your website.

Use case:

• Users reading about how SEO can improve ROI can easily navigate back to the homepage if they want to learn more about your services or company.

10. Consider linking https://thatware.co/seo-companies-can-help-companies-recover-roi/ to https://thatware.co/services/

Explanation:

• Linking the same page (seo-companies-can-help-companies-recover-roi/) to the services page can drive users from informative content to your actual service offerings.
• Why is this important? If someone is interested in learning about SEO’s impact on ROI, they might also be interested in hiring SEO services. This link makes it easier for them to find that next step.

11. Consider linking https://thatware.co/on-page-audit/ to https://thatware.co/

Explanation:

• This recommendation links the on-page-audit/ page to the homepage. The on-page-audit/ page likely focuses on auditing specific on-page SEO elements, and linking it back to the homepage helps improve navigation.
• Why is this important? It improves site structure and helps both users and search engines understand how the on-page audit fits into your overall services.

12. Consider linking https://thatware.co/on-page-audit/ to https://thatware.co/services/

Explanation:

• Linking the on-page-audit/ page to your services page encourages users interested in an audit to explore your paid services.
• Why is this important? This can drive traffic from users interested in audits to potentially paying for your services.

13. Consider linking https://thatware.co/traffic-acquisition-plan/ to https://thatware.co/

Explanation:

• This page likely talks about traffic acquisition strategies, and linking it to the homepage makes it easier for users to navigate back to the main site.

14. Consider linking https://thatware.co/traffic-acquisition-plan/ to https://thatware.co/services/

Explanation:

• Linking the traffic acquisition plan page to the services page can help users explore how they can use your services to grow their website traffic.

15. Consider linking https://thatware.co/40-deep-seo-insights/ to https://thatware.co/

Explanation:

• The page 40-deep-seo-insights/ is likely a guide or article about SEO. Linking it back to the homepage provides easy navigation for users.

16. Consider linking https://thatware.co/40-deep-seo-insights/ to https://thatware.co/services/

Explanation:

• Linking a deep SEO insights article to your services page encourages users to explore your professional SEO services after learning from the insights.

Steps You Need to Take as a Website Owner:

1.    Follow the Recommendations:

• The recommendations provided by the model are clear actions you should take on your website. For each recommendation (like linking seo-services-brighton to services), you should go into your content management system (CMS) or have your web developer add internal links between the pages as suggested.

Why is this important?

• Internal linking improves your website’s structure and helps search engines crawl and understand the relationships between your pages. This is important for SEO because Google values websites that are easy to navigate, with strong connections between related pages.

2.    Monitor the Changes:

• After making these changes, keep an eye on your website’s performance in terms of SEO rankings and traffic. It may take a few weeks to see changes, but improving internal links can positively impact both user experience and search engine ranking.

3.    Explain to Your Client:

• If you need to explain this to your client, you can say:
  • “The model has successfully analyzed the structure of your website and provided recommendations on how to improve internal links. By adding these internal links between specific pages, we can make it easier for both users and search engines to navigate your website, which will ultimately help improve your search engine rankings.”

4.    Continue Optimizing:

• Internal linking is an ongoing process. You should continue to monitor your website and, as you add new content or pages, ensure they are well-linked to other relevant pages.

Summary of What You Need to Do:

• Implement the suggested links the model provides (like linking healthcare-seo-services to your homepage).
• Ensure your internal links make sense for users and search engines (this helps with SEO).
• Monitor your SEO performance to see how the changes impact rankings and traffic.

This is a very actionable output that you can easily work with to improve your website’s internal linking structure.

What Does This Mean for Your SEO?

·         Internal linking plays a key role in SEO because it helps Google understand which pages on your site are most important. Some pages may not rank well if they are isolated and not linked to others. By following the recommendations, you are ensuring that your most important content is well-connected.

·         User Experience: Internal links also improve user navigation. When users can easily find related content, they are more likely to stay on your site longer, which is another positive signal for SEO.

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Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker.

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