DPGNN: Dual-Perception Graph Neural Network for Representation Learning
This work addresses performance bottlenecks in graph representation learning for researchers and practitioners, though it appears incremental as it builds on existing GNN frameworks.
The paper tackles limitations in graph neural networks (GNNs) by proposing a novel message-passing paradigm that addresses inflexible message sources, node-level discrepancies, and single-space restrictions, resulting in a Dual-Perception Graph Neural Network (DPGNN) that outperforms state-of-the-art models on six benchmark datasets.
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.