Graphs are powerful data structures that can represent complex relationships and interactions between entities. Graph learning techniques allow us to extract meaningful information from graphs, discover patterns, and make predictions.
- Directed Graphs: Graphs where edges have a direction (from one node to another).
- Weighted Graphs: Graphs where edges have associated weights or values.
- Connected Graphs: Graphs where there is a path between any two nodes.
- Types of Graphs: Examples include social networks, recommendation systems, knowledge graphs, etc.
- Objects: Nodes or vertices in the graph.
- Graph Measures: Metrics to quantify properties of the graph (e.g., degree centrality, betweenness centrality).
- Adjacency Matrix: A matrix representation of the graph.
- Breadth-First Search (BFS): Traversal technique to explore nodes level by level.
- Depth-First Search (DFS): Traversal technique to explore nodes as deeply as possible.
- Word2Vec: A popular embedding technique for representing nodes in a graph.
- CBOW vs. Skip-gram: Two variants of Word2Vec.
- Skip-gram Model: A neural network architecture for learning node embeddings.
- Deep Walk and Random Walk: Techniques to generate node sequences for training embeddings.
Feel free to explore more about graph learning and apply these concepts to your specific use cases!
- To run the code related to graph learning, you’ll need the following Python packages:
- Make sure to install these packages using pip:
pip install networkx python-socket pytorch-geometry