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Graph data is powerful, thanks to its ability to model arbitrary
relationship between objects and is encountered in a range of
real-world applications in fields such as bioinformatics, traffic
network, scientific collaboration, world wide web and social
networks. Graph data mining is used to discover useful information
and knowledge from graph data. The complications of nodes, links
and the semi-structure form present challenges in terms of the
computation tasks, e.g., node classification, link prediction, and
graph classification. In this context, various advanced techniques,
including graph embedding and graph neural networks, have recently
been proposed to improve the performance of graph data mining. This
book provides a state-of-the-art review of graph data mining
methods. It addresses a current hot topic - the security of graph
data mining - and proposes a series of detection methods to
identify adversarial samples in graph data. In addition, it
introduces readers to graph augmentation and subgraph networks to
further enhance the models, i.e., improve their accuracy and
robustness. Lastly, the book describes the applications of these
advanced techniques in various scenarios, such as traffic networks,
social and technical networks, and blockchains.
Graph data is powerful, thanks to its ability to model arbitrary
relationship between objects and is encountered in a range of
real-world applications in fields such as bioinformatics, traffic
network, scientific collaboration, world wide web and social
networks. Graph data mining is used to discover useful information
and knowledge from graph data. The complications of nodes, links
and the semi-structure form present challenges in terms of the
computation tasks, e.g., node classification, link prediction, and
graph classification. In this context, various advanced techniques,
including graph embedding and graph neural networks, have recently
been proposed to improve the performance of graph data mining. This
book provides a state-of-the-art review of graph data mining
methods. It addresses a current hot topic - the security of graph
data mining - and proposes a series of detection methods to
identify adversarial samples in graph data. In addition, it
introduces readers to graph augmentation and subgraph networks to
further enhance the models, i.e., improve their accuracy and
robustness. Lastly, the book describes the applications of these
advanced techniques in various scenarios, such as traffic networks,
social and technical networks, and blockchains.
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