Graph Data Mining

1. Front Matter

   Pages i-xvi

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2. Information Source Estimation with Multi-Channel Graph Neural Network

   • Xincheng Shu, Bin Yu, Zhongyuan Ruan, Qingpeng Zhang, Qi Xuan

   Pages 1-27

3. Link Prediction Based on Hyper-Substructure Network

   • Jian Zhang, Jinyin Chen, Qi Xuan

   Pages 29-48

4. Broad Learning Based on Subgraph Networks for Graph Classification

   • Jinhuan Wang, Pengtao Chen, Yunyi Xie, Yalu Shan, Qi Xuan, Guanrong Chen

   Pages 49-71

5. Subgraph Augmentation with Application to Graph Mining

   • Jiajun Zhou, Jie Shen, Yalu Shan, Qi Xuan, Guanrong Chen

   Pages 73-91

6. Adversarial Attacks on Graphs: How to Hide Your Structural Information

   • Yalu Shan, Junhao Zhu, Yunyi Xie, Jinhuan Wang, Jiajun Zhou, Bo Zhou et al.

   Pages 93-120

7. Adversarial Defenses on Graphs: Towards Increasing the Robustness of Algorithms

   • Huiling Xu, Ran Gan, Tao Zhou, Jinhuan Wang, Jinyin Chen, Qi Xuan

   Pages 121-154

8. Understanding Ethereum Transactions via Network Approach

   • Yunyi Xie, Jiajun Zhou, Jinhuan Wang, Jian Zhang, Yunxuan Sheng, Jiajing Wu et al.

   Pages 155-176

9. Find Your Meal Pal: A Case Study on Yelp Network

   • Jian Zhang, Jie Xia, Laijian Li, Binda Shen, Jinhuan Wang, Qi Xuan

   Pages 177-188

10. Graph Convolutional Recurrent Neural Networks: A Deep Learning Framework for Traffic Prediction

    • Dongwei Xu, Hongwei Dai, Qi Xuan

    Pages 189-204

11. Time Series Classification Based on Complex Network

    • Kunfeng Qiu, Jinchao Zhou, Hui Cui, Zhuangzhi Chen, Shilian Zheng, Qi Xuan

    Pages 205-222

12. Exploring the Controlled Experiment by Social Bots

    • Yong Min, Yuying Zhou, Tingjun Jiang, Ye Wu

    Pages 223-243

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 Mining
• Graph Data
• Data Mining
• Link Prediction
• Node Classification
• Graph Classification
• Complex Network
• Traffic Network
• Social Network
• Social Bot
• Blockchain
• Graph Neural Network
• Graph Embedding
• Adversarial Attack
• Graph Augmentation

• Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China

  Qi Xuan, Zhongyuan Ruan, Yong Min

Qi Xuan is a Professor at the Institute of Cyberspace Security,  Zhejiang University of Technology, Hangzhou, China. His current research interests include network science, graph data mining, cyberspace security, and deep learning. He has published more than 50 papers in leading journals and conferences, including IEEE TKDE, IEEE TIE, IEEE TNSE, ICSE, and FSE. He is the reviewer of the journals such like IEEE TKDE, IEEE TIE, IEEE TII, and IEEE TNSE.

Zhongyuan Ruan is a lecturer at the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His current research interests include network science, such as epidemic and information spreading in complex networks, and traffic networks. He has published more than 20 papers in journals such as Physical Review Letters, Physical Review E, Chaos, Scientific Reports, and Physica A.

Yong Min is an Associate Professor at the Institute of Cyberspace Security, Zhejiang University ofTechnology, Hangzhou, China. His research interests include social network analysis, computational communication, and artificial intelligence algorithms. He was named an Excellent Young Teacher of Zhejiang University of Technology. He has hosted and participated in more than ten projects, including those by national and provincial natural science foundations. He has also published over 30 papers, including two in the leading journal Nature and Science, and he holds more than three patents.

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