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.

翻译：图神经网络（GNN）通过编码从社交网络到生物网络等不同领域中网络的有意义拓扑结构，在节点级和图级任务上取得了巨大成功。然而，重复的聚合操作会导致节点表示过度混合，尤其是在具有多个GNN层的密集区域，导致嵌入表示几乎无法区分。这种现象引发了过度平滑问题，阻碍了下游图分析任务。为克服此问题，我们提出了一种新颖且灵活的基于Truss的图稀疏化模型，该模型通过修剪图中密集区域的边来解决问题。在密集区域修剪冗余边有助于防止GNN模型在分层消息传递和池化过程中聚合过多的邻域信息。随后，我们将该稀疏化模型应用于最先进的基线GNN和池化模型，如GIN、SAGPool、GMT、DiffPool、MinCutPool、HGP-SL、DMonPool和AdamGNN。在不同真实数据集上的大量实验表明，我们的模型在图形分类任务中显著提升了基线GNN模型的性能。