Self-Organization Preserved Graph Structure Learning with Principle of Relevant Information
Self-Organization Preserved Graph Structure Learning with Principle of Relevant Information
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, …