第4期 杨宇迪，等：异质信息网络中基于网络嵌入的影响力最大化 ·765· [10]TANG Jang,TANG Xueyan,YUAN Junsong.An effi- [20]SHI Chuan,HU Binbin,ZHAO W X,et al.Heterogen- cient and effective hop-based approach for influence max- eous information network embedding for recommenda- imization in social networks[J].Social network analysis tion[J].IEEE transactions on knowledge and data engin- and mining,2018,8(1):10. eering,2019,31(2):357-370. [11]PENG Sancheng,YANG Aimin,CAO Lihong,et al.So- [21]SHI Yu,ZHU Qi,GUO Fang,et al.Easing embedding cial influence modeling using information theory in mo- learning by comprehensive transcription of heterogen- bile social networks[J].Information sciences,2017,379: eous information networks[C]//Proceedings of the 24th 146-159 ACM SIGKDD International Conference on Knowledge [12]SHI Chuan,KONG Xiangnan,HUANG Yue,et al. 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[17]WAN Mengting,OUYANG Yunbo,KAPLAN L,et al. Graph regularized meta-path based transductive regres- 周丽华，教授，博士生导师，CCF sion in heterogeneous information network[C]//Proceed- 会员，主要研究方向为数据挖掘、多视 ings of the 2015 SIAM International Conference on Data 角学习、异质社交网络分析。主持国 Mining.Vancouver,Canada,2015:918-926. 家自然科学基金项目3项、云南省重 [18]YANG Yudi,ZHOU Lihua,JIN Zhao,et al.Meta path- 点基金项目1项。发表学术论文 based information entropy for modeling social influence 80余篇，出版学术著作2部。 in heterogeneous information networks[C]//Proceedings of the 20th IEEE International Conference on Mobile 杜国王，博土研究生，主要研究方 Data Management.Hong Kong,China,2019:557-562. 向为数据挖掘、多视角聚类。 [19]LIU Zemin,ZHENG V W,ZHAO Zhou,et al.Distance- aware DAG embedding for proximity search on hetero- geneous graphs[C]//Proceedings of 32nd AAAI Confer- ence on Artificial Intelligence (AAAI).New Orleans, USA.2018:2355-2362.TANG Jang, TANG Xueyan, YUAN Junsong. An effi￾cient and effective hop-based approach for influence max￾imization in social networks[J]. Social network analysis and mining, 2018, 8(1): 10. [10] PENG Sancheng, YANG Aimin, CAO Lihong, et al. So￾cial influence modeling using information theory in mo￾bile social networks[J]. Information sciences, 2017, 379: 146–159. [11] SHI Chuan, KONG Xiangnan, HUANG Yue, et al. HeteSim: a general framework for relevance measure in heterogeneous networks[J]. IEEE transactions on know￾ledge and data engineering, 2014, 26(10): 2479–2492. [12] WANG Chenguang, SUN Yizhou, SONG Yanglei, et al. RelSim: relation similarity search in schema-rich hetero￾geneous information networks[C]//Proceedings of the 2016 SIAM International Conference on Data Mining. Philadelphia, USA, 2016: 621−629. [13] CHEN Lu, GAO Yunjun, ZHANG Yuanliang, et al. Effi￾cient and incremental clustering algorithms on star￾schema heterogeneous graphs[C]//Proceedings of 35th In￾ternational Conference on Data Engineering. Macao, China, 2019: 256−267. [14] CHEN Junxiang, DAI Wei, SUN Yizhou, et al. Cluster￾ing and ranking in heterogeneous information networks via gamma-Poisson model[C]//Proceedings of the 2015 SIAM International Conference on Data Mining. Van￾couver, Canada, 2015: 425−432. [15] BANGCHAROENSAP P, MURATA T, KOBAYASHI H, et al. Transductive classification on heterogeneous in￾formation networks with edge betweenness-based normal￾ization[C]//Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. San Fran￾cisco, USA, 2016: 437−446. [16] WAN Mengting, OUYANG Yunbo, KAPLAN L, et al. Graph regularized meta-path based transductive regres￾sion in heterogeneous information network[C]//Proceed￾ings of the 2015 SIAM International Conference on Data Mining. Vancouver, Canada, 2015: 918−926. [17] YANG Yudi, ZHOU Lihua, JIN Zhao, et al. Meta path￾based information entropy for modeling social influence in heterogeneous information networks[C]//Proceedings of the 20th IEEE International Conference on Mobile Data Management. Hong Kong, China, 2019: 557−562. [18] LIU Zemin, ZHENG V W, ZHAO Zhou, et al. Distance￾aware DAG embedding for proximity search on hetero￾geneous graphs[C]//Proceedings of 32nd AAAI Confer￾ence on Artificial Intelligence (AAAI). New Orleans, USA, 2018: 2355−2362. [19] SHI Chuan, HU Binbin, ZHAO W X, et al. Heterogen￾eous information network embedding for recommenda￾tion[J]. IEEE transactions on knowledge and data engin￾eering, 2019, 31(2): 357–370. [20] SHI Yu, ZHU Qi, GUO Fang, et al. Easing embedding learning by comprehensive transcription of heterogen￾eous information networks[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London, UK, 2018: 2190−2199. [21] LUO Chen, GUAN Renchu, WANG Zhe, et al. Het￾PathMine: a novel transductive classification algorithm on heterogeneous information networks[C]//Proceedings of the 36th European Conference on Information Retrieval. Amsterdam, The Netherlands, 2014: 210−221. [22] FU Taoyang, LEE W C, LEI Zhen. HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Manage￾ment. Singapore, 2017: 1797−1806. [23] SUN Yizhou, HAN Jiawei, JING Gao, et al. iTopicMod￾el: information network-integrated topic modeling[C]// Proceedings of the 9th IEEE International Conference on Data Mining. Miami, USA, 2009: 493−502. [24] 马知恩, 周义仓, 王稳地, 等. 传染病动力学的数学建模 与研究 [M]. 北京: 科学出版社, 2014. [25] 作者简介： 杨宇迪，硕士研究生，主要研究方 向为社会网络分析、数据挖掘。 周丽华，教授，博士生导师，CCF 会员，主要研究方向为数据挖掘、多视 角学习、异质社交网络分析。主持国 家自然科学基金项目 3 项、云南省重 点基金项 目 1 项。发表学术论 文 80 余篇，出版学术著作 2 部。 杜国王，博士研究生，主要研究方 向为数据挖掘、多视角聚类。 第 4 期 杨宇迪，等：异质信息网络中基于网络嵌入的影响力最大化 ·765·