Graph Representation Learning

The goal of graph representation learning is to construct a set of features (embeddings) to represent the structure of the graph and the data on it. These embeddings can be categorized into node embeddings, edge embeddings, and graph embeddings, which are used to represent each node, each edge, and the entire graph, respectively. This approach can effectively capture complex relationships and structural information, providing powerful tools for graph data analysis.