SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling

TL;DR

SPGNN introduces enhanced graph convolution and a novel pooling method to better recognize complex subgraph patterns, significantly improving graph classification performance.

Contribution

The paper proposes a concatenation-based convolution and WL-SortPool pooling module, advancing subgraph pattern recognition in GNNs.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Effectively characterizes complex graph topology.

Outperforms existing GNN and kernel methods.

Abstract

Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. GNNs effectively implement node representation learning through neighborhood aggregation and achieve impressive results in many graph-related tasks. However, most neighborhood aggregation approaches are summation-based, which can be problematic as they may not be sufficiently expressive to encode informative graph structures. Furthermore, though the graph pooling module is also of vital importance for graph learning, especially for the task of graph classification, research on graph down-sampling mechanisms is rather limited. To address the above challenges, we propose a concatenation-based graph convolution mechanism that injectively updates node representations to maximize the discriminative power in distinguishing non-isomorphic subgraphs. In addition, we design a novel graph pooling module, called WL-SortPool, to learn important subgraph patterns in a deep-learning manner. WL-SortPool layer-wise sorts node representations (i.e. continuous WL colors) to separately learn the relative importance of subtrees with different depths for the purpose of classification, thus better characterizing the complex graph topology and rich information encoded in the graph. We propose a novel Subgraph Pattern GNN (SPGNN) architecture that incorporates these enhancements. We test the proposed SPGNN architecture on many graph classification benchmarks. Experimental results show that our method can achieve highly competitive results with state-of-the-art graph kernels and other GNN approaches.