AutoGEL: An Automated Graph Neural Network with Explicit Link Information
This work addresses the need for more effective automated GNNs for graph-based tasks, but it is incremental as it builds on existing AutoGNN methods by adding explicit link modeling.
The paper tackles the problem of automated graph neural network (AutoGNN) design by introducing AutoGEL, which explicitly models link information to improve performance on link prediction, node classification, and graph classification tasks, achieving superior results on benchmark datasets.
Recently, Graph Neural Networks (GNNs) have gained popularity in a variety of real-world scenarios. Despite the great success, the architecture design of GNNs heavily relies on manual labor. Thus, automated graph neural network (AutoGNN) has attracted interest and attention from the research community, which makes significant performance improvements in recent years. However, existing AutoGNN works mainly adopt an implicit way to model and leverage the link information in the graphs, which is not well regularized to the link prediction task on graphs, and limits the performance of AutoGNN for other graph tasks. In this paper, we present a novel AutoGNN work that explicitly models the link information, abbreviated to AutoGEL. In such a way, AutoGEL can handle the link prediction task and improve the performance of AutoGNNs on the node classification and graph classification task. Specifically, AutoGEL proposes a novel search space containing various design dimensions at both intra-layer and inter-layer designs and adopts a more robust differentiable search algorithm to further improve efficiency and effectiveness. Experimental results on benchmark data sets demonstrate the superiority of AutoGEL on several tasks.