Directed Homophily-Aware Graph Neural Network

• URL: http://arxiv.org/abs/2505.22362v2
• Date: Fri, 30 May 2025 15:21:24 GMT
• Title: Directed Homophily-Aware Graph Neural Network
• Authors: Aihu Zhang, Jiaxing Xu, Mengcheng Lan, Shili Xiang, Yiping Ke,
• Abstract summary: We propose Directed Homophily-aware Graph Neural Network (DHGNN), a novel framework that incorporates homophily-aware and direction-sensitive components.<n>DHGNN employs a resettable gating mechanism to adaptively modulate message contributions based on homophily levels and informativeness.<n>Our analysis shows that the gating mechanism captures directional homophily gaps and fluctuating homophily across layers, providing deeper insights into message-passing behavior on complex graph structures.
• Score: 7.539052660225002
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Graph Neural Networks (GNNs) have achieved significant success in various learning tasks on graph-structured data. Nevertheless, most GNNs struggle to generalize to heterophilic neighborhoods. Additionally, many GNNs ignore the directional nature of real-world graphs, resulting in suboptimal performance on directed graphs with asymmetric structures. In this work, we propose Directed Homophily-aware Graph Neural Network (DHGNN), a novel framework that addresses these limitations by incorporating homophily-aware and direction-sensitive components. DHGNN employs a resettable gating mechanism to adaptively modulate message contributions based on homophily levels and informativeness, and a structure-aware noise-tolerant fusion module to effectively integrate node representations from the original and reverse directions. Extensive experiments on both homophilic and heterophilic directed graph datasets demonstrate that DHGNN outperforms state-of-the-art methods in node classification and link prediction. In particular, DHGNN improves over the best baseline by up to 15.07% in link prediction. Our analysis further shows that the gating mechanism captures directional homophily gaps and fluctuating homophily across layers, providing deeper insights into message-passing behavior on complex graph structures.

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