Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach

TL;DR

This paper introduces a truss-based graph sparsification method to address oversmoothing in GNNs by pruning dense regions, significantly enhancing graph classification performance across various datasets.

Contribution

The paper presents a novel truss-based sparsification model that effectively prunes dense graph regions to mitigate oversmoothing in GNNs, improving their performance.

Findings

Improved accuracy of GNNs on multiple real-world datasets.

Effective reduction of oversmoothing through edge pruning.

Enhanced performance of baseline GNN models in graph classification.

Abstract

Graph Neural Network (GNN) achieves great success for node-level and graph-level tasks via encoding meaningful topological structures of networks in various domains, ranging from social to biological networks. However, repeated aggregation operations lead to excessive mixing of node representations, particularly in dense regions with multiple GNN layers, resulting in nearly indistinguishable embeddings. This phenomenon leads to the oversmoothing problem that hampers downstream graph analytics tasks. To overcome this issue, we propose a novel and flexible truss-based graph sparsification model that prunes edges from dense regions of the graph. Pruning redundant edges in dense regions helps to prevent the aggregation of excessive neighborhood information during hierarchical message passing and pooling in GNN models. We then utilize our sparsification model in the state-of-the-art baseline GNNs and pooling models, such as GIN, SAGPool, GMT, DiffPool, MinCutPool, HGP-SL, DMonPool, and AdamGNN. Extensive experiments on different real-world datasets show that our model significantly improves the performance of the baseline GNN models in the graph classification task.