Label-independent Information Compression for Heterogeneous Graph Representation

doi:10.3969/j.issn.1006-2475.2025.04.006

Abstract

Abstract: The existing methods for heterogeneous graph （HG） representation are mostly based on powerful graph neural networks， which aggregate semantic information within and between meta-paths to embed nodes. However， these existing approaches overlook the heterogeneity of nodes in HG， causing irrelevant information from neighboring nodes to spread along graph structures to higher-order nodes， disturbing the HG representation. To overcome this problem， this paper proposes a heterogeneous graph representation method called Label-Independent Compression for Heterogeneous Graph （LICHGR）. The core idea of LICHGR is， under the guidance of the Information Bottleneck， to utilize the Hilbert-Schmidt Independence Criterion to restrict the propagation of label-independent information in heterogeneous graph while preserving label-dependent information as much as possible. Specifically， LICHGR constructs multi-faceted label-independent compression constraints among input features， hidden features within meta-paths， and true labels， extracting rich label-dependent information to enhance the quality of heterogeneous graph representation. Multiple experiments designed on three public datasets validate the effectiveness of LICHGR.

Key words: graph neural network, heterogeneous graph representation, information bottleneck, Hilbert-Schmidt independence criterion

CLC Number:

TP391

MA Jian, WANG Yifei, MENG Li, HE Yunfei, YANG Fei. Label-independent Information Compression for Heterogeneous Graph Representation[J]. Computer and Modernization, 2025, 0(04): 36-41.

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