Evolutionary Graph Fusion Architecture Search

The great success of graph neural networks (GNNs) in graph-structured data tasks benefits from the powerful structure learning abilities of their architectures. For complex datasets, too deep GNNs can suffer from over-smoothing problem, leading to degradation of prediction performance. Recently, a graph fusion architecture designed by domain experts mitigates the over-smoothing problem. However, this design paradigm is labor and computation-intensive. Drawing on the idea of neural architecture search, this paper proposes evolutionary graph fusion architecture search (EGFAS), which can automatically find graph fusion architecture with the capability to solve the over-smoothing problem. Specifically, we design a graph fusion architecture search space, which includes multiple aggregation functions and fusion operations. An efficient encoding method is used to represent candidate architectures. Furthermore, we propose a surrogate model to solve the expensive overhead of performance evaluation. Experiments on five benchmark datasets are carried out to confirm the superiority of the proposed algorithm. It is shown that the proposed method leads to better results comparing to the state-of-the-art algorithms in terms of search effectiveness and adaptability to different datasets.