Weighted Graph Structure Learning with Attention Denoising for Node Classification
This work addresses node classification in weighted graphs for applications like social networks or recommendation systems, representing an incremental improvement by combining existing techniques with modifications.
The paper tackles the problem of noisy edges and anomalous edge weights in weighted graphs that hinder node classification by proposing the Edge Weight-aware Graph Structure Learning (EWGSL) method, which redefines attention coefficients and applies graph structure learning, resulting in an average Micro-F1 improvement of 17.8% over baselines.
Node classification in graphs aims to predict the categories of unlabeled nodes by utilizing a small set of labeled nodes. However, weighted graphs often contain noisy edges and anomalous edge weights, which can distort fine-grained relationships between nodes and hinder accurate classification. We propose the Edge Weight-aware Graph Structure Learning (EWGSL) method, which combines weight learning and graph structure learning to address these issues. EWGSL improves node classification by redefining attention coefficients in graph attention networks to incorporate node features and edge weights. It also applies graph structure learning to sparsify attention coefficients and uses a modified InfoNCE loss function to enhance performance by adapting to denoised graph weights. Extensive experimental results show that EWGSL has an average Micro-F1 improvement of 17.8% compared with the best baseline.