IVQ-GNN: Mitigating Performance Gap from Graph Connection Pattern Inconsistency via Vector Quantization

Di Jin, Yixuan Du, Cuiying Huo, Xiaotong Huang, Ruqiong Zhang, Xiaobao Wang, Yawen Li

Abstract:

Heterophily in graphs is a key challenge for Graph Neural Networks (GNNs). By proposing various homophily measures, recent
work has provided insights into how heterophily affects node classification. However, while both graph homophily and heterophily
can be further refined into diverse connection patterns, previous
work has largely overlooked the role of connection pattern inconsistency. In this paper, we delve deeper into heterophily and
homophily by shifting from coarse-grained heterophily ratios to
a unified, fine-grained formulation based on connection patterns,
and we further reveal an uneven distribution and a train–test gap
of these patterns. Empirical studies indicate that this inconsistency
leads to severe performance disparity. To address this issue, we
propose a novel two-stage method named IVQ-GNN. In the pretraining phase, IVQ-GNN encodes diverse connection patterns into
a codebook that serves as an orthogonal basis for the representation space. In the fine-tuning phase, a self-attention module linearly
combines these orthogonal bases to expand the learned token space
of connection patterns, thereby improving adaptation to rare and
out-of-distribution (OOD) patterns. Experimental results on multiple datasets demonstrate that IVQ-GNN significantly improves model performance and validate that the proposed method effectively addresses the connection pattern inconsistency. Our code is
available at https://github.com/Duyx5149/IVQ-GNN.