On the Cross-type Homophily of Heterogeneous Graphs: Understanding and Unleashing
Zhen Tao, Ziyue Qiao, Chaoqi Chen, Zhengyi Yang, Lun Du, Qingqiang Sun*
ACM International Conference on Information and Knowledge Management (CIKM)
RAIDS Lab Authors
Details
Research Area
Tags
Resources
Abstract
Homophily, the tendency of similar nodes to connect, is a fundamental phenomenon in network science and a critical factor in the performance of graph neural networks (GNNs). While existing studies primarily explore homophily in homogeneous graphs, where nodes share the same type, real-world networks are often more accurately modeled as heterogeneous graphs (HGs) with diverse node types and intricate cross-type interactions. This structural diversity complicates the analysis of homophily, as traditional homophily metrics fail to account for distinct label spaces across node types. To address this limitation, we introduce the Cross-Type Homophily Ratio (CHR), a novel metric that quantifies homophily based on the similarity of target information across different node types. Additionally, we propose Cross-Type Homophily-guided Graph Editing (CTHGE), a novel method for improving heterogeneous graph neural networks (HGNNs) performance by optimizing cross-type connectivity using Cross-Type Homophily Ratio. Extensive experiments on five HG datasets with nine HGNNs validate the effectiveness of CTHGE, which delivers a maximum relative performance improvement of over 25% for HGNNs on node classification tasks, offering a fresh perspective on cross-type homophily in HGs learning.
Author Affiliations
BibTeX
@inproceedings{tao2025homophily,
title = {On the Cross-type Homophily of Heterogeneous Graphs: Understanding and Unleashing},
author = {Tao, Zhen and Qiao, Ziyue and Chen, Chaoqi and Yang, Zhengyi and Du, Lun and Sun, Qingqiang},
series = {CIKM '25},
url = {http://dx.doi.org/10.1145/3746252.3761008},
doi = {10.1145/3746252.3761008},
booktitle = {Proceedings of the 34th ACM International Conference on Information and Knowledge Management},
publisher = {ACM},
year = {2025},
month = Nov,
pages = {2842-2852},
collection = {CIKM '25}
}