Cross-modal feature symbiosis for personalized meta-path generation in heterogeneous networks

Abstract

In heterogeneous graph neural networks (HGNNs), the capture of intricate relationships among various types of entities is essential to achieve advanced machine learning applications. Heterogeneous Information Networks (HINs), composed of interconnected multi-type nodes and edges, face significant challenges in managing semantic diversity and inherent heterogeneity. Traditional methods, which rely on manually designed meta-paths, struggle to adapt dynamically to personalized needs and often neglect the integration of structural and attribute features. To address these limitations, this paper introduces the Cross-Modal Symbiotic Meta-Path Generator (CSMPG) framework. CSMPG integrates two key modules: a Cross-Modal State Generation Module that encodes node structure and attribute information into task-aware state vectors and a Personalized Meta-Path Generation Module that dynamically generates and refines meta-paths using reinforcement learning. By leveraging downstream task feedback, CSMPG optimizes path selection to maximize performance. The framework effectively balances cross-modal feature integration and semantic diversity, uncovering impactful meta-paths that are often overlooked by traditional approaches. Experimental results demonstrate that CSMPG consistently enhances recommendation quality and significantly outperforms structure-only and predefined-path-based models.