Clarify Confused Nodes via Separated Learning
CoRR(2023)
摘要
Graph neural networks (GNNs) have achieved remarkable advances in
graph-oriented tasks. However, real-world graphs invariably contain a certain
proportion of heterophilous nodes, challenging the homophily assumption of
classical GNNs and hindering their performance. Most existing studies continue
to design generic models with shared weights between heterophilous and
homophilous nodes. Despite the incorporation of high-order messages or
multi-channel architectures, these efforts often fall short. A minority of
studies attempt to train different node groups separately but suffer from
inappropriate separation metrics and low efficiency. In this paper, we first
propose a new metric, termed Neighborhood Confusion (NC), to facilitate a more
reliable separation of nodes. We observe that node groups with different levels
of NC values exhibit certain differences in intra-group accuracy and visualized
embeddings. These pave the way for Neighborhood Confusion-guided Graph
Convolutional Network (NCGCN), in which nodes are grouped by their NC values
and accept intra-group weight sharing and message passing. Extensive
experiments on both homophilous and heterophilous benchmarks demonstrate that
our framework can effectively separate nodes and yield significant performance
improvement compared to the latest methods. The source code will be released
soon.
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