TSGCN: A Framework for Hierarchical Graph Representation Learning

Recently, there has been a growing demand for advances in representation learning for graphs. The literature has developed methods to represent nodes in an embedding space, allowing for classical techniques to perform node classification and prediction. One such method is the graph convolutional neural network that aggregates the node neighbor's features to create the embedding. In this method, the embedding contains local information about an individual's connections but lacks the global community dynamics about that individual. We propose a method that leverages both local and global information, offering significant advancements in the analysis of social networks. We first represent information across the entire hierarchy of the network by allowing the graph convolutional network to skip neighbors in its convolutions. We propose 3 methods of skipping that leverage matrix-powers of the adjacency matrix and a breadth-first search traversal. Once convolutions are performed, we capture correlations across the hierarchies by constructing our convolutions into a tensor (e.g., multi-way array), enabling a more holistic understanding of individual nodes' roles within their communities. We present experimental results for the proposed method and compare/contrast with other state-of-the-art methods in benchmark social network datasets for node classification and link prediction tasks. Ultimately, the proposed method not only advances the field of graph representation learning but also demonstrates improved performance across various complex social networks.