DPGNN: Dual-Perception Graph Neural Network for Representation Learning

• URL: http://arxiv.org/abs/2110.07869v3
• Date: Tue, 23 Jan 2024 12:50:42 GMT
• Title: DPGNN: Dual-Perception Graph Neural Network for Representation Learning
• Authors: Li Zhou, Wenyu Chen, Dingyi Zeng, Shaohuan Cheng, Wanlong Liu, Malu Zhang, Hong Qu
• Abstract summary: Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks. Most existing GNNs are based on the message-passing paradigm to iteratively aggregate neighborhood information in a single topology space. We present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction.
• Score: 21.432960458513826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning on graphs. However, most existing GNNs are based on the message-passing paradigm to iteratively aggregate neighborhood information in a single topology space. Despite their success, the expressive power of GNNs is limited by some drawbacks, such as inflexibility of message source expansion, negligence of node-level message output discrepancy, and restriction of single message space. To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction. To verify its validity, we instantiate the new message-passing paradigm as a Dual-Perception Graph Neural Network (DPGNN), which applies a node-to-step attention mechanism to aggregate node-specific multi-step neighborhood information adaptively. Our proposed DPGNN can capture the structural neighborhood information and the feature-related information simultaneously for graph representation learning. Experimental results on six benchmark datasets with different topological structures demonstrate that our method outperforms the latest state-of-the-art models, which proves the superiority and versatility of our method. To our knowledge, we are the first to consider node-specific message passing in the GNNs.

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