HeSRN: Representation Learning On Heterogeneous Graphs via Slot-Aware Retentive Network

• URL: http://arxiv.org/abs/2510.09767v1
• Date: Fri, 10 Oct 2025 18:18:06 GMT
• Title: HeSRN: Representation Learning On Heterogeneous Graphs via Slot-Aware Retentive Network
• Authors: Yifan Lu, Ziyun Zou, Belal Alsinglawi, Islam Al-Qudah, Izzat Alsmadi, Feilong Tang, Pengfei Jiao, Shoaib Jameel,
• Abstract summary: HeSRN is a novel Heterogeneous Slot-aware Retentive Network for efficient and expressive heterogeneous graph representation learning.<n>HeSRN consistently outperforms state-of-the-art heterogeneous graph neural networks and Graph Transformer baselines on node classification tasks.
• Score: 22.60005673964228
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Transformers have recently achieved remarkable progress in graph representation learning by capturing long-range dependencies through self-attention. However, their quadratic computational complexity and inability to effectively model heterogeneous semantics severely limit their scalability and generalization on real-world heterogeneous graphs. To address these issues, we propose HeSRN, a novel Heterogeneous Slot-aware Retentive Network for efficient and expressive heterogeneous graph representation learning. HeSRN introduces a slot-aware structure encoder that explicitly disentangles node-type semantics by projecting heterogeneous features into independent slots and aligning their distributions through slot normalization and retention-based fusion, effectively mitigating the semantic entanglement caused by forced feature-space unification in previous Transformer-based models. Furthermore, we replace the self-attention mechanism with a retention-based encoder, which models structural and contextual dependencies in linear time complexity while maintaining strong expressive power. A heterogeneous retentive encoder is further employed to jointly capture both local structural signals and global heterogeneous semantics through multi-scale retention layers. Extensive experiments on four real-world heterogeneous graph datasets demonstrate that HeSRN consistently outperforms state-of-the-art heterogeneous graph neural networks and Graph Transformer baselines on node classification tasks, achieving superior accuracy with significantly lower computational complexity.

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