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Graph Neural Networks: Graph Transformationget domain, which requires to learn a transformation mapping from the source to target domains. For example, it is important to study how structural connectivity influences functional connectivity in brain networks and traffic networks. It is also common to study how a protein (e.g., a network of at

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Graph Neural Networks: Graph Structure Learningplications such as Natural Language Processing, Computer Vision, recommender systems, drug discovery and so on. However, the great success of GNNs relies on the quality and availability of graph-structured data which can either be noisy or unavailable. The problem of graph structure learning aims to

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Dynamic Graph Neural Networks crucial building-block for machine learning applications; the nodes of the graph correspond to entities and the edges correspond to interactions and relations. The entities and relations may evolve; e.g., new entities may appear, entity properties may change, and new relations may be formed between

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Graph Representation Learningn of graph representation learning. Afterwards and primarily, we provide a comprehensive overview of a large number of graph representation learning methods in a systematic manner, covering the traditional graph representation learning, modern graph representation learning, and graph neural networks.