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Esun Selvam

doi:10.1016/j.cattod.2026.115890

Multi-prior anchored graph neural networks for robust and adaptive representation learning

2026-07-31

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Yuhang Wu

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Junfen Chen *

DOI：10.1007/s13042-026-03251-w

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Graph Neural Networks (GNNs) excel in structured data analysis but struggle with real-world heterogeneous graphs-characterized by distinct substructures and sparse inter-substructure connections-which hinders local-global information fusion, while existing methods, limited by the inherent locality of neighborhood aggregation or prohibitive computational costs, face critical challenges in efficient and robust global modeling of structurally heterogeneous graphs; this paper proposes MPA-GNN with two core designs: multi-prior anchor initialization to cover heterogeneous substructures and a sparse Node $$\rightleftarrows $$ Anchor mechanism for low-cost inter-substructure propagation, and experiments on heterogeneous datasets show its competitive or superior performance, while ablation studies confirm the contributions of key modules, making MPA-GNN a unified, efficient solution for robust heterogeneous graph representation learning. Source code of MPA-GNN is freely available at: https://github.com/wuyuhang1107-arch/MPA-GNN .

Keywords:

Graph neural network

Anchor-based learning

Node classification

Structural heterogeneity

Multi-prior anchored graph neural networks for robust and adaptive representation learning

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