Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification

TL;DR

This paper introduces LRGNN, a stacking-based GNN architecture optimized via neural architecture search to effectively capture long-range dependencies in graph classification without altering the original graph structure.

Contribution

It proposes a novel NAS-driven method for designing data-specific stacking GNNs that address long-range dependencies while avoiding over-smoothing issues.

Findings

LRGNN achieves state-of-the-art performance on five datasets.

Data-specific GNN architectures better capture long-range dependencies.

Stacking GNNs with adaptive skip-connections outperform shallow GNNs.

Abstract

In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task. Currently, shallow GNNs are more common due to the well-known over-smoothing problem facing deeper GNNs. However, they are sub-optimal without utilizing the information from distant nodes, i.e., the long-range dependencies. The mainstream methods in the graph classification task can extract the long-range dependencies either by designing the pooling operations or incorporating the higher-order neighbors, while they have evident drawbacks by modifying the original graph structure, which may result in information loss in graph structure learning. In this paper, by justifying the smaller influence of the over-smoothing problem in the graph classification task, we evoke the importance of stacking-based GNNs and then employ them to capture the long-range dependencies without modifying the original graph structure. To achieve this, two design needs are given for stacking-based GNNs, i.e., sufficient model depth and adaptive skip-connection schemes. By transforming the two design needs into designing data-specific inter-layer connections, we propose a novel approach with the help of neural architecture search (NAS), which is dubbed LRGNN (Long-Range Graph Neural Networks). Extensive experiments on five datasets show that the proposed LRGNN can achieve the best performance, and obtained data-specific GNNs with different depth and skip-connection schemes, which can better capture the long-range dependencies.