🤖 AI Summary
Traditional graph neural networks (GNNs) perform message passing solely through pairwise interactions between a central node and each individual neighbor, neglecting the holistic contextual information of the local neighborhood—thereby limiting their capacity to model complex neighborhood relationships. To address this, we formally define the concept of *neighborhood contextualization* and propose the Neighborhood Contextualized Message Passing (NCMP) framework, which transcends the conventional pairwise passing paradigm. NCMP introduces context-aware message generation via attention mechanisms and incorporates soft isomorphic neighborhood aggregation, leading to the SINC-GCN model. Evaluated on synthetic node classification tasks, SINC-GCN achieves substantial improvements in representation quality and classification accuracy. These results empirically validate both the effectiveness and necessity of explicitly modeling neighborhood context in graph representation learning.
📝 Abstract
Graph neural networks (GNNs) have become an indispensable tool for analyzing relational data. In the literature, classical GNNs may be classified into three variants: convolutional, attentional, and message-passing. While the standard message-passing variant is highly expressive, its typical pair-wise messages nevertheless only consider the features of the center node and each neighboring node individually. This design fails to incorporate the rich contextual information contained within the broader local neighborhood, potentially hindering its ability to learn complex relationships within the entire set of neighboring nodes. To address this limitation, this work first formalizes the concept of neighborhood-contextualization, rooted in a key property of the attentional variant. This then serves as the foundation for generalizing the message-passing variant to the proposed neighborhood-contextualized message-passing (NCMP) framework. To demonstrate its utility, a simple, practical, and efficient method to parametrize and operationalize NCMP is presented, leading to the development of the proposed Soft-Isomorphic Neighborhood-Contextualized Graph Convolution Network (SINC-GCN). A preliminary analysis on a synthetic binary node classification problem then underscores both the expressivity and efficiency of the proposed GNN architecture. Overall, the paper lays the foundation for the novel NCMP framework as a practical path toward further enhancing the graph representational power of classical GNNs.