Graph Spectral Embedding using the Geodesic Betweeness Centrality

TL;DR

The paper introduces Graph Sylvester Embedding (GSE), a novel unsupervised graph representation method that captures complex network structures by combining multiple basis functions derived from edge centrality measures.

Contribution

GSE is a new graph embedding technique that integrates multiple basis functions from edge centrality to improve network structure representation.

Findings

GSE outperforms state-of-the-art methods in predicting failed edges in materials.

GSE improves accuracy in network alignment tasks in biological networks.

The method effectively captures both local and global graph features.

Abstract

We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure. GSE uses the solution of the Sylvester equation to capture both network structure and neighborhood proximity in a single representation. Unlike embeddings based on the eigenvectors of the Laplacian, GSE incorporates two or more basis functions, for instance using the Laplacian and the affinity matrix. Such basis functions are constructed not from the original graph, but from one whose weights measure the centrality of an edge (the fraction of the number of shortest paths that pass through that edge) in the original graph. This allows more flexibility and control to represent complex network structure and shows significant improvements over the state of the art when used for data analysis tasks such as predicting failed edges in material science and network alignment in the human-SARS CoV-2 protein-protein interactome.