GRAIN通过多粒度与隐式信息聚合，提升异质图的节点表征效果。
GRAIN: Multi-Granular and Implicit Information Aggregation Graph Neural Network for Heterophilous Graphs
- 引入多粒度与远距离隐式关系信息融合机制
- 在13个数据集上超越12种先进模型，异质图表现优异
- 适合处理标签或特征差异大的复杂异质图任务
图神经网络（GNN）在图表示学习中表现突出，但近期研究表明，当图中相连节点在特征或标签上存在差异（即异质图）时，GNN常无法超越简单MLP，这挑战了同质性假设。现有方法往往忽视信息粒度的重要性，且很少考虑远距离节点间的隐式关联。为此，我们提出专为异质图设计的粒度与隐式图网络（GRAIN），通过在不同粒度层级上聚合多视角信息，并融入非邻接远端节点的隐含数据，有效整合局部与全局信息，生成更平滑、准确的节点嵌入。我们还设计了一种自适应图信息聚合器，高效融合多粒度与隐式信息，实验在13个涵盖不同同质/异质程度的数据集上验证其优势，结果表明GRAIN持续优于12种先进模型，无论在同质图还是异质图上均表现出色。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) have shown significant success in learning graph representations. However, recent studies reveal that GNNs often fail to outperform simple MLPs on heterophilous graph tasks, where connected nodes may differ in features or labels, challenging the homophily assumption. Existing methods addressing this issue often overlook the importance of information granularity and rarely consider implicit relationships between distant nodes. To overcome these limitations, we propose the Granular and Implicit Graph Network (GRAIN), a novel GNN model specifically designed for heterophilous graphs. GRAIN enhances node embeddings by aggregating multi-view information at various granularity levels and incorporating implicit data from distant, non-neighboring nodes. This approach effectively integrates local and global information, resulting in smoother, more accurate node representations. We also introduce an adaptive graph information aggregator that efficiently combines multi-granularity and implicit data, significantly improving node representation quality, as shown by experiments on 13 datasets covering varying homophily and heterophily. GRAIN consistently outperforms 12 state-of-the-art models, excelling on both homophilous and heterophilous graphs.
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