SCNode: Spatial and Contextual Coordinates for Graph Representation Learning

TL;DR

SCNode introduces a novel spatial and contextual embedding framework for GNNs, improving node representations in both homophilic and heterophilic graphs by addressing limitations like oversquashing and oversmoothing.

Contribution

The paper presents SCNode, a new framework that integrates spatial and contextual information, along with homophily matrices, to enhance GNN performance across diverse graph types.

Findings

SCNode outperforms traditional GNNs on benchmark datasets.

It demonstrates robustness in both homophilic and heterophilic graphs.

The approach effectively mitigates oversquashing and oversmoothing issues.

Abstract

Effective node representation lies at the heart of Graph Neural Networks (GNNs), as it directly impacts their ability to perform downstream tasks such as node classification and link prediction. Most existing GNNs, particularly message passing graph neural networks, rely on neighborhood aggregation to iteratively compute node embeddings. While powerful, this paradigm suffers from well-known limitations of oversquashing, oversmoothing, and underreaching that degrade representation quality. More critically, MPGNNs often assume homophily, where connected nodes share similar features or labels, leading to poor generalization in heterophilic graphs where this assumption breaks down. To address these challenges, we propose \textit{SCNode}, a \textit{Spatial-Contextual Node Embedding} framework designed to perform consistently well in both homophilic and heterophilic settings. SCNode integrates spatial and contextual information, yielding node embeddings that are not only more discriminative but also structurally aware. Our approach introduces new homophily matrices for understanding class interactions and tendencies. Extensive experiments on benchmark datasets show that SCNode achieves superior performance over conventional GNN models, demonstrating its robustness and adaptability in diverse graph structures.