Automated graph construction and graph neural network search: a survey

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Graphs are widely used to describe objects and their interactions in physically-informed real-world networking scenario including transportation, networking and energy, etc. Graph neural network (GNN) is the latest deep learning (DL) model for processing graph-structured data, widely applied in various tasks, e.g., prediction, classification/fault detection, and decision-making problems. However, GNNs based tasks still face two major challenges. First, GNN’s powerful ability in learning expressive graph representations relies on the availability and quality of graph structures, which practically, are often noisy, incomplete, or even unavailable. Second, various GNN architectures are typically designed in a manually heuristic manner, which not only requires specified knowledge of domain expert, but inevitably leads to sub-optimal performance due to insufficient exploration. To address the above issues, this survey presents a unified view of automated graph neural network design, integrating graph structure learning and graph neural architecture search (GNAS) into a common pipeline for downstream tasks. Specifically, our work first systematically categorizes the graph structure learning paradigms from the perspective of task-agnostic heuristic based strategy and task-aware (end-to-end) learning based strategy. Then, GNAS steps are comprehensively synthesized from the following aspects: micro and macro search spaces, architecture embedding, search strategies, and performance evaluation methodologies. Their applicability and limitations are thoroughly analyzed. Moreover, the interaction paradigms between graph structure learning and GNAS architecture search are conceptually outlined. Finally, open issues in automated GNAS are summarized. This survey aims to provide a thoughtful insight for the automated representation learning of graph neural networks from both perspectives of data i.e., graph structure, and models i.e., automatic GNN architectures.