个人简介:北京邮电大学计算机学院教授、博士研究生导师、智能通信软件与多媒体北京市重点实验室副主任。主要研究方向: 数据挖掘、机器学习、人工智能和演化计算。近五年来,作为第一作者或通信作者发表高水平学术论文50余篇,英文专著一部,包括数据挖掘领域的顶级期刊和会议IEEE TKDE、ACM TIST、KDD、AAAI、IJCAI、WWW等,相关研究成果应用于阿里巴巴、腾讯、华为等公司。获得ADMA2011/AMDA2018国际会议最佳论文奖、CCF-腾讯犀牛鸟基金及项目优秀奖,并指导学生获得顶尖国际数据挖掘竞赛IJCAI Contest 2015 全球冠军。获得北京市高等学校青年英才和师德先锋等称号。

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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

    1. Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S. Yu, Tianyi Wu. PathSim: Meta Path-Based Top-k Similarity Search in Heterogeneous Information Networks. VLDB Endowment, vol.4 pp. 992-1003, 2011
    1. Yizhou Sun, Yintao Yu, Jiawei Han. Ranking-Based Clustering of Heterogeneous Information Networks with Star Network Schema. KDD 2009: 797-806.
    1. Huang Z, Zheng Y, Cheng R, et al. Meta structure: Computing relevance in large heterogeneous information networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 1595-1604.
    1. YuXiao Dong, Nitesh V. Chawla, Ananthram Swami. Metapath2vec: Scalable Representation Learning for Heterogeneous Networks. KDD 2017.
    1. Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. WSDM 2018.
    1. Tao-yang Fu, Wang-Chien Lee, Zhen Lei. HIN2vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning. CIKM 2017.
    1. 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
    1. Chuan Shi, Xiangnan Kong, Yitong Li, Philip S. Yu, Bin Wu. HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks, IEEE Transactions on Knowledge and Data Engineering, 2014.
    1. Chuan Shi, Philip S. Yu. Heterogeneous Information Network Analysis and Applications. Springer. ISBN 978-3-319-56211-7. 2017.
    1. Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, Philip S. Yu. A survey on Heterogeneous Information Network Analysis. IEEE Transactions on Knowledge and Data Engineering, 29(1), 17-37, 2017.
    1. Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, Yongliang Li Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation. KDD2019
    1. Binbin Hu, Yuan Fang, Chuan Shi. Adversarial Learning on Heterogeneous Information Networks. KDD 2019
    1. Chuan Shi, Binbin Hu, Wayne Xin Zhao, Philip S. Yu. Heterogeneous Information Network Embedding for Recommendation. IEEE Transactions on Knowledge and Data Engineering, 2018.
    1. Xiao Wang, Houye Ji, Chuan Shi, et al. Heterogeneous Graph Attention Network. WWW 2019.
    1. 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.
    1. Yuanfu Lu, Chuan Shi, Linmei Hu, Zhiyuan Liu. Relation Structure-Aware Heterogeneous Information Network Embedding. AAAI 2019.
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