EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning

• URL: http://arxiv.org/abs/2502.02302v1
• Date: Tue, 04 Feb 2025 13:16:54 GMT
• Title: EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning
• Authors: Shengda Zhuo, Jiwang Fang, Hongguang Lin, Yin Tang, Min Chen, Changdong Wang, Shuqiang Huang,
• Abstract summary: Graph Neural Networks (GNNs) have significant advantages in handling non-Euclidean data. The inclusion of multidimensional edge information enhances the functionality and flexibility of the GNN model. Experiments on four real-world heterogeneous graphs demonstrate the effectiveness of theproposed model.
• Score: 12.767227925026972
• License:
• Abstract: Graph Neural Networks (GNNs) have significant advantages in handling non-Euclidean data and have been widely applied across various areas, thus receiving increasing attention in recent years. The framework of GNN models mainly includes the information propagation phase and the aggregation phase, treating nodes and edges as information entities and propagation channels, respectively. However, most existing GNN models face the challenge of disconnection between node and edge feature information, as these models typically treat the learning of edge and node features as independent tasks. To address this limitation, we aim to develop an edge-empowered graph feature preference learning framework that can capture edge embeddings to assist node embeddings. By leveraging the learned multidimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features, thereby obtaining the non-local structural characteristics and fine-grained high-order node features. Specifically, the inclusion of multidimensional edge information enhances the functionality and flexibility of the GNN model, enabling it to handle complex and diverse graph data more effectively. Additionally, integrating relational representation learning into the message passing framework allows graph nodes to receive more useful information, thereby facilitating node representation learning. Finally, experiments on four real-world heterogeneous graphs demonstrate the effectiveness of theproposed model.

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