Disambiguated Node Classification with Graph Neural Networks
Tianxiang Zhao Xiang Zhang Suhang Wang
Graph Neural Networks (GNNs) have demonstrated significant success in
learning from graph-structured data across various domains. Despite their great
successful, one critical challenge is often overlooked by existing works, i.e.,
the learning of message propagation that can generalize effectively to
underrepresented graph regions. These minority regions often exhibit irregular
homophily/heterophily patterns and diverse neighborhood class distributions,
resulting in ambiguity. In this work, we investigate the ambiguity problem
within GNNs, its impact on representation learning, and the development of
richer supervision signals to fight against this problem. We conduct a
fine-grained evaluation of GNN, analyzing the existence of ambiguity in
different graph regions and its relation with node positions. To disambiguate
node embeddings, we propose a novel method, {\method}, which exploits
additional optimization guidance to enhance representation learning,
particularly for nodes in ambiguous regions. {\method} identifies ambiguous
nodes based on temporal inconsistency of predictions and introduces a
disambiguation regularization by employing contrastive learning in a
topology-aware manner. {\method} promotes discriminativity of node
representations and can alleviating semantic mixing caused by message
propagation, effectively addressing the ambiguity problem. Empirical results
validate the efficiency of {\method} and highlight its potential to improve GNN
performance in underrepresented graph regions.