Principle of Relevant Information for Graph Sparsification

Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, Jose C Principe

Keywords: graph sparsification, principle of relevant information, multi-task regression learning, brain network classification

TL;DR: We extend the principle of relevant information (PRI) to graph data; we also evaluate Graph-PRI on the problem of graph sparsification and other real-world applications.

Abstract: Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties. In this paper, we propose the first general and effective information-theoretic formulation of graph sparsification, by taking inspiration from the Principle of Relevant Information (PRI). To this end, we extend the PRI from a standard scalar random variable setting to structured data (i.e., graphs). Our Graph-PRI objective is achieved by operating on the graph Laplacian, made possible by expressing the graph Laplacian of a subgraph in terms of a sparse edge selection vector $\mathbf{w}$. We provide both theoretical and empirical justifications on the validity of our Graph-PRI. We also analyze its analytical solutions in a few special cases. We finally present three representative real-world applications, namely graph sparsification, graph regularized multi-task learning, and medical imaging-derived brain network classification, to demonstrate the effectiveness, the versatility and the enhanced interpretability of our approach over prevalent sparsification techniques. Code of Graph-PRI is available at https://github.com/SJYuCNEL/PRI-Graphs

Community Implementations: [ 4 code implementations](https://www.catalyzex.com/paper/principle-of-relevant-information-for-graph/code)