[1]YANG Yudi,ZHOU Lihua,DU Guowang,et al.Influence maximization based on network embedding in heterogeneous information networks[J].CAAI Transactions on Intelligent Systems,2021,16(4):757-765.[doi:10.11992/tis.202009047]
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Influence maximization based on network embedding in heterogeneous information networks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 4 Page number: 757-765 Column: 学术论文—知识工程 Public date: 2021-07-05
Title:
Influence maximization based on network embedding in heterogeneous information networks
Author(s):
YANG Yudi1;  ZHOU Lihua1; 2;  DU Guowang1;  ZOU Xingzhu1;  DING Haiyan1
1. School of Information, Yunnan University, Kunming 650504, China;
2. Dianchi College, Yunnan University, Kunming 650228, China
Keywords:
heterogeneous information network; homogeneous information network; influence maximization; information diffusion; network embedding; direct influence; indirect influence; global influence
CLC:
TP391
DOI:
10.11992/tis.202009047
Abstract:
Most current influence maximization algorithms ignore the problem that heterogeneous information networks contain multiple node types and relationship types, and different types of nodes cannot be measured in the original workspace. Accordingly, to solve these issues, this paper proposes a novel model for influence maximization based on network embedding in heterogeneous information networks, which helps to realize influence maximization by choosing initial diffusion nodes. The model can not only manifest the potential information in heterogeneous information networks while encoding it but also capture the uncertainty and complexity of influence among different types of nodes. Experimental results on three real datasets demonstrate the effectiveness of the proposed model.
References:
[1] HEIDARI M, ASADPOUR M, FAILI H. SMG: fast scalable greedy algorithm for influence maximization in social networks[J]. Physica a: statistical mechanics and its applications, 2015, 420: 124-133.
[2] ORIEDI D, DE RUNZ C, GUESSOUM Z, et al. Influence maximization through user interaction modeling[C]//Proceedings of the 35th Annual ACM Symposium on Applied Computing. Brno, Czech Republic, 2020: 1888-1890.
[3] LIU Wei, CHEN Ling, CHEN Xin, et al. An algorithm for influence maximization in competitive social networks with unwanted users[J]. Applied intelligence, 2020, 50(2): 417-437.
[4] KIANIAN S, ROSTAMNIA M. An efficient path-based approach for influence maximization in social networks[J]. Expert systems with applications, 2021, 167: 114-168.
[5] SHANG Jiaxing, ZHOU Shangbo, LI Xin, et al. CoFIM: a community-based framework for influence maximization on large-scale networks[J]. Knowledge-based systems, 2017, 117: 88-100.
[6] 胡庆成, 张勇, 邢春晓. 基于有重叠社区划分的社会网络影响最大化方法研究[J]. 计算机科学, 2018, 45(6): 32-35
HU Qingcheng, ZHANG Yong, XING Chunxiao, et al. K-clique heuristic algorithm for influence maximization in social network[J]. Computer science, 2018, 45(6): 32-35
[7] KEMPE D, KLEINBERG J, TARDOS é. Maximizing the spread of influence through a social network[C]//Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, USA, 2003: 137-146.
[8] LESKOVEC J, KRAUSE A, GUESTRIN C, et al. Cost-effective outbreak detection in networks[C]// Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, USA, 2007: 420-429.
[9] GOYAL A, LU Wei, LAKSHMANAN L V S. CELF++: optimizing the greedy algorithm for influence maximization in social networks[C]//Proceedings of the 20th International Conference Companion on World Wide Web. Hyderabad, India, 2011: 47-48.
[10] TANG Jang, TANG Xueyan, YUAN Junsong. An efficient and effective hop-based approach for influence maximization in social networks[J]. Social network analysis and mining, 2018, 8(1): 10.
[11] PENG Sancheng, YANG Aimin, CAO Lihong, et al. Social influence modeling using information theory in mobile social networks[J]. Information sciences, 2017, 379: 146-159.
[12] SHI Chuan, KONG Xiangnan, HUANG Yue, et al. HeteSim: a general framework for relevance measure in heterogeneous networks[J]. IEEE transactions on knowledge and data engineering, 2014, 26(10): 2479-2492.
[13] WANG Chenguang, SUN Yizhou, SONG Yanglei, et al. RelSim: relation similarity search in schema-rich heterogeneous information networks[C]//Proceedings of the 2016 SIAM International Conference on Data Mining. Philadelphia, USA, 2016: 621-629.
[14] CHEN Lu, GAO Yunjun, ZHANG Yuanliang, et al. Efficient and incremental clustering algorithms on star-schema heterogeneous graphs[C]//Proceedings of 35th International Conference on Data Engineering. Macao, China, 2019: 256-267.
[15] CHEN Junxiang, DAI Wei, SUN Yizhou, et al. Clustering and ranking in heterogeneous information networks via gamma-Poisson model[C]//Proceedings of the 2015 SIAM International Conference on Data Mining. Vancouver, Canada, 2015: 425-432.
[16] BANGCHAROENSAP P, MURATA T, KOBAYASHI H, et al. Transductive classification on heterogeneous information networks with edge betweenness-based normalization[C]//Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. San Francisco, USA, 2016: 437-446.
[17] WAN Mengting, OUYANG Yunbo, KAPLAN L, et al. Graph regularized meta-path based transductive regression in heterogeneous information network[C]//Proceedings of the 2015 SIAM International Conference on Data Mining. Vancouver, Canada, 2015: 918-926.
[18] 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.
[19] LIU Zemin, ZHENG V W, ZHAO Zhou, et al. Distance-aware DAG embedding for proximity search on heterogeneous graphs[C]//Proceedings of 32nd AAAI Conference on Artificial Intelligence (AAAI). New Orleans, USA, 2018: 2355-2362.
[20] SHI Chuan, HU Binbin, ZHAO W X, et al. Heterogeneous information network embedding for recommendation[J]. IEEE transactions on knowledge and data engineering, 2019, 31(2): 357-370.
[21] SHI Yu, ZHU Qi, GUO Fang, et al. Easing embedding learning by comprehensive transcription of heterogeneous information networks[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London, UK, 2018: 2190-2199.
[22] LUO Chen, GUAN Renchu, WANG Zhe, et al. HetPathMine: 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.
[23] 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 Management. Singapore, 2017: 1797-1806.
[24] SUN Yizhou, HAN Jiawei, JING Gao, et al. iTopicModel: information network-integrated topic modeling[C]//Proceedings of the 9th IEEE International Conference on Data Mining. Miami, USA, 2009: 493-502.
[25] 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.
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