大多数实际系统由大量相互作用的、多类型的组件组成,而大多数当代研究将它们建模为同构的信息网络,没有区分网络中不同类型的对象和链接。近年来,越来越多的研究者开始将这些相互联系、多类型的数据视为异构信息网络(HIN),并利用网络中对象和链接结构类型的丰富语义,开发结构化分析方法。此外,近年来在深度学习和网络嵌入方面的进展给HIN挖掘带来了新的机遇和挑战,异构网络嵌入,甚至异构图神经网络也成为热点。在本教程中,我们将介绍异构信息网络分析的最新发展,特别是新出现的异构网络嵌入。
教程资料
- 第一部分:介绍
- 第二部分:基于元路径的数据挖掘
- 第三部分:异构信息网路嵌入
- 第四部分:应用
- 第五部分:结论与未来工作
参考资料列表
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- Binbin Hu, Chuan Shi, Xin Zhao, Philip S. Yu. Leveraging Meta-path based Context for Top-N Recommendation with A Neural Co-Attention Model. KDD 2018
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- Chuan Shi, Philip S. Yu. Heterogeneous Information Network Analysis and Applications. Springer. ISBN 978-3-319-56211-7. 2017.
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- Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, Yongliang Li Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation. KDD2019
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- Binbin Hu, Yuan Fang, Chuan Shi. Adversarial Learning on Heterogeneous Information Networks. KDD 2019
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- Chuan Shi, Binbin Hu, Wayne Xin Zhao, Philip S. Yu. Heterogeneous Information Network Embedding for Recommendation. IEEE Transactions on Knowledge and Data Engineering, 2018.
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- Xiao Wang, Houye Ji, Chuan Shi, et al. Heterogeneous Graph Attention Network. WWW 2019.
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- Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, Yuan Qi. Cash-out User Detection based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism. AAAI 2019.
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- Yuanfu Lu, Chuan Shi, Linmei Hu, Zhiyuan Liu. Relation Structure-Aware Heterogeneous Information Network Embedding. AAAI 2019.