An machine learning algorithm to check the state of web graph and make it have intuition

So, today as usual I asked ChatGPT (GPT-4) to answer my question.

My question was:

How would you approach to make a way to make a machine learning algorithm to check the state of the web graph’s number (depth etc) and create a sort of AI intuition? Can you give me examples of related studies?

The answer that came back from ChatGPT was:

To develop a machine learning algorithm that checks the state of the web graph, including its depth and other characteristics, and to endow it with a sort of AI “intuition,” we would need to approach this multi-faceted problem through a series of strategic steps. The end goal would be to create an AI system capable of understanding the complexities of the web’s structure and dynamics, which is critical for tasks such as web search optimization, content analysis, and internet topology research.

Step 1: Define the Problem and ObjectivesObjective: To monitor and analyze the state of the web graph in real-time or near-real-time to infer its properties such as depth, connectivity, centrality, and other relevant metrics.Intuition Development: Aim to develop algorithms that can predict changes, identify emerging patterns, or suggest optimizations in the web’s structure.

Step 2: Data Collection and PreprocessingCrawl the Web: Use web crawlers to gather data on web pages and their links. This data will form the web graph, where nodes represent web pages, and edges represent hyperlinks between them.Graph Construction: From the collected data, construct a graph. This involves preprocessing steps such as cleaning (removing dead links), normalization (standardizing node names), and deduplication.Feature Extraction: Identify and extract features relevant to the graph’s state analysis, such as PageRank, clustering coefficient, shortest path lengths, and degree distribution.

Step 3: Algorithm DevelopmentBaseline Models: Implement existing graph analysis algorithms as baseline models. This could include algorithms for computing centrality measures, community detection, and graph traversal.Machine Learning Models: Develop or adapt machine learning models to predict the graph’s properties based on extracted features. This could involve supervised learning models if historical data is available, or unsupervised models to detect patterns or anomalies in the graph structure.Intuition Module: For developing AI intuition, consider reinforcement learning or deep learning models that can make decisions or predictions based on incomplete information, learning from the outcomes of their previous predictions.

Step 4: Validation and RefinementBacktesting: Use historical web graph data to test the model’s predictions against known outcomes. This helps in refining the models.Real-time Testing: Deploy the model in a controlled environment to monitor its performance in real-time, allowing for further adjustments.Step 5: Implementation and Continuous LearningIntegration: Integrate the system into existing web analysis tools or platforms.Feedback Loop: Implement mechanisms to incorporate new data and feedback into the model, allowing it to adapt to changes in the web graph over time.Examples of Related StudiesGoogle’s PageRank: Though not a machine learning algorithm, PageRank is an essential study in understanding web graphs. It calculates the importance of web pages based on the links between them.Graph Neural Networks (GNNs): Recent studies involve using GNNs for tasks like link prediction, graph classification, and node classification, which are crucial for analyzing web graphs.Anomaly Detection in Web Graphs: Research in detecting sudden changes or anomalies in the web graph can be particularly relevant. Such studies often employ machine learning to identify patterns that deviate from the norm.Predicting the Evolution of the Web: Some studies focus on predicting how the web graph will evolve, using historical data and machine learning models to forecast changes in the graph’s structure.For practical applications and further reading, looking into the proceedings of conferences such as the World Wide Web Conference (WWW) and journals like the Journal of Web Semantics could be beneficial. These sources frequently publish cutting-edge research on web graphs and machine learning applications in web and network analysis.

Me:

I will use this as a starting point to make an algorithm that uses machine learning to speed up and traverse the web faster. It is always good to note that shortest path is not necessarily the best path for anything. Some say going the long way is actually the shortcut. Quite philosophical, I think.