Multi-view graph pooling via dominant sets for graph classification

Graph pooling is a fundamental operation in Graph Neural Networks (GNNs), designed to simplify graphs by reducing the number of nodes and edges while preserving essential structural information for classification tasks. However, most existing pooling methods tend to overlook edge weights and rely on a single-view pooling strategy that focuses either on local or global topological information, failing to capture the full structural context of the graph. To address these limitations, this study introduces a novel Dominant Set Multi-View Pooling (DSMVPool) method featuring two main contributions. First, we propose a dominant-set cluster pooling approach that analyzes the overall graph architecture and connectivity patterns, identifies potential clusters using edge weight information, and generates a coarser graph view. In addition, we create two complementary pooled views by selecting the most representative nodes based on local topology and node features. Second, we design a fusion-view attention layer that integrates the coarser graph structure with the pooled graph views, enabling our method to simultaneously capture and combine global and local structural information and node features. Extensive experiments on four graph classification benchmarks, covering computer vision, chemical, biological, and social networks, demonstrate that DSMVPool achieves superior performance compared to state-of-the-art methods.