Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities

TL;DR

This paper introduces multi-level attention pooling (MLAP), a novel GNN architecture that unifies local and global graph information across layers, enhancing graph classification performance by preserving multi-scale structural features.

Contribution

The paper proposes MLAP, a new pooling method that captures multiple levels of locality in graphs, addressing the limitations of deep GNNs and oversmoothing.

Findings

MLAP improves graph classification accuracy over baseline models.

Layer-wise representations with MLAP enhance discriminability of graph features.

Aggregating multi-level information benefits graph representation learning.

Abstract

Graph neural networks (GNNs) have been widely used to learn vector representation of graph-structured data and achieved better task performance than conventional methods. The foundation of GNNs is the message passing procedure, which propagates the information in a node to its neighbors. Since this procedure proceeds one step per layer, the range of the information propagation among nodes is small in the lower layers, and it expands toward the higher layers. Therefore, a GNN model has to be deep enough to capture global structural information in a graph. On the other hand, it is known that deep GNN models suffer from performance degradation because they lose nodes' local information, which would be essential for good model performance, through many message passing steps. In this study, we propose multi-level attention pooling (MLAP) for graph-level classification tasks, which can adapt to both local and global structural information in a graph. It has an attention pooling layer for each message passing step and computes the final graph representation by unifying the layer-wise graph representations. The MLAP architecture allows models to utilize the structural information of graphs with multiple levels of localities because it preserves layer-wise information before losing them due to oversmoothing. Results of our experiments show that the MLAP architecture improves the graph classification performance compared to the baseline architectures. In addition, analyses on the layer-wise graph representations suggest that aggregating information from multiple levels of localities indeed has the potential to improve the discriminability of learned graph representations.