Self-organization Preserved Graph Structure Learning with Principle of Relevant Information
This addresses a fundamental limitation in graph learning for applications relying on accurate node relationships, though it appears incremental as it builds on existing graph structure learning methods.
The paper tackles the problem of incomplete, noisy, or redundant real-world graph structures in Graph Neural Networks by proposing PRI-GSL, a framework that reveals inherent graph structures using the Principle of Relevant Information, achieving superior effectiveness and robustness in experiments.
Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption cannot always be satisfied, as real-world graphs are always incomplete, noisy, or redundant. How to reveal the inherent graph structure in a unified way remains under-explored. We proposed PRI-GSL, a Graph Structure Learning framework guided by the Principle of Relevant Information, providing a simple and unified framework for identifying the self-organization and revealing the hidden structure. PRI-GSL learns a structure that contains the most relevant yet least redundant information quantified by von Neumann entropy and Quantum Jensen-Shannon divergence. PRI-GSL incorporates the evolution of quantum continuous walk with graph wavelets to encode node structural roles, showing in which way the nodes interplay and self-organize with the graph structure. Extensive experiments demonstrate the superior effectiveness and robustness of PRI-GSL.