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Balancing Augmentation with Edge-Utility Filter for Signed GNNs by Chen, Ke-Jia; Ji, Yaming; Qu, Youran; Xu, Chuhan is a scholarly article available to read on EtoBox.

What is Balancing Augmentation with Edge-Utility Filter for Signed GNNs about?

Signed graph neural networks (SGNNs) has recently drawn more attention as many real-world networks are signed networks containing two types of edges: positive and negative. The existence of negative edges affects the SGNN robustness on two aspects. One is the semantic imbalance as the negative edges are usually hard to obtain though they can provide potentially useful information. The other is the structural unbalance, e.g. unbalanced triangles, an indication of incompatible relationship among nodes. In this paper, we propose a balancing augmentation method to address the above two aspects for SGNNs. Firstly, the utility of each negative edge is measured by calculating its occurrence in unbalanced structures. Secondly, the original signed graph is selectively augmented with the use of (1) an edge perturbation regulator to balance the number of positive and negative edges and to determine the ratio of perturbed edges to original edges and (2) an edge utility filter to remove the negative edges with low utility to make the graph structure more balanced. Finally, a SGNN is trained on the augmented graph which effectively explores the credible relationships. A detailed theoretical anal

Author

Chen, Ke-Jia; Ji, Yaming; Qu, Youran; Xu, Chuhan

Published

2023

Language

EN