Hierarchical Graph Pooling with Structure Learning (1911.05954v3)

Abstract: Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in numerous graph related tasks. However, existing GNN models mainly focus on designing graph convolution operations. The graph pooling (or downsampling) operations, that play an important role in learning hierarchical representations, are usually overlooked. In this paper, we propose a novel graph pooling operator, called Hierarchical Graph Pooling with Structure Learning (HGP-SL), which can be integrated into various graph neural network architectures. HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs. More specifically, the graph pooling operation adaptively selects a subset of nodes to form an induced subgraph for the subsequent layers. To preserve the integrity of graph's topological information, we further introduce a structure learning mechanism to learn a refined graph structure for the pooled graph at each layer. By combining HGP-SL operator with graph neural networks, we perform graph level representation learning with focus on graph classification task. Experimental results on six widely used benchmarks demonstrate the effectiveness of our proposed model.

Hierarchical Graph Pooling with Structure Learning

Graph Neural Networks (GNNs) have become pivotal in processing graph-structured data, yet a significant aspect—the pooling mechanism—is often underexplored. The paper "Hierarchical Graph Pooling with Structure Learning" addresses this gap by proposing a novel operator named Hierarchical Graph Pooling with Structure Learning (HGP-SL). This operator integrates graph pooling and structure learning within a unified framework, enhancing hierarchical representation learning for graph classification tasks.

Graph Pooling

Unlike conventional GNN models that predominantly focus on convolution operations, this paper emphasizes the importance of pooling operations in capturing hierarchical graph representations. The proposed HGP-SL operator introduces an adaptive graph pooling mechanism that selects nodes based on a node information score, computed using Manhattan distance. This score considers both node features and graph topological information, ensuring the preservation of informative nodes while forming induced subgraphs.

Structure Learning

To preserve key topological information, the authors propose a structure learning mechanism using sparse attention. This mechanism refines the graph structure by learning underlying pairwise node relationships. By leveraging sparsemax, a variant of the softmax function, the structure learning component achieves sparse distributions that better capture the essential substructures within the original graphs.

Experimental Evaluation

The authors validate the efficacy of HGP-SL through experiments on six benchmark datasets, demonstrating notable advancements in graph classification accuracy compared to existing methods. The non-parametric nature of the proposed pooling operation ensures ease of implementation without introducing additional optimization parameters.

Implications and Future Directions

The coupling of pooling and structure learning as proposed in HGP-SL holds both theoretical and practical implications for advancing graph representation learning. The paper illustrates the ability to preserve key subgraph structures, a crucial aspect when considering real-world applications such as protein network analysis or social network recommendations. Furthermore, the adaptation of sparse attention mechanisms opens avenues for more efficient and scalable models in graph learning tasks.

Future research could explore integrating HGP-SL with diverse neural network architectures beyond GCNs, such as GraphSAGE or GAT, to assess generalizability across various graph convolutional paradigms. Additionally, extending this approach for tasks beyond graph classification, such as link prediction or anomaly detection, represents a promising direction for further exploration.

In conclusion, the paper skillfully underscores the importance of comprehensive pooling mechanisms in enhancing the representation capabilities of GNNs. Through experimental validation and innovative methodology, HGP-SL emerges as a robust tool to address the nuanced challenges of graph representation learning.