Influence Maximization on Social Graphs: A Survey
Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technica...
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| Published in: | IEEE transactions on knowledge and data engineering Vol. 30; no. 10; pp. 1852 - 1872 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1041-4347, 1558-2191 |
| Online Access: | Get full text |
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| Abstract | Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective, with a special focus on the following key aspects: (1) a review of well-accepted diffusion models that capture the information diffusion process and build the foundation of the IM problem, (2) a fine-grained taxonomy to classify existing IM algorithms based on their design objectives, (3) a rigorous theoretical comparison of existing IM algorithms, and (4) a comprehensive study on the applications of IM techniques in combining with novel context features of social networks such as topic, location, and time. Based on this analysis, we then outline the key challenges and research directions to expand the boundary of IM research. |
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| AbstractList | Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective, with a special focus on the following key aspects: (1) a review of well-accepted diffusion models that capture the information diffusion process and build the foundation of the IM problem, (2) a fine-grained taxonomy to classify existing IM algorithms based on their design objectives, (3) a rigorous theoretical comparison of existing IM algorithms, and (4) a comprehensive study on the applications of IM techniques in combining with novel context features of social networks such as topic, location, and time. Based on this analysis, we then outline the key challenges and research directions to expand the boundary of IM research. |
| Author | Li, Yuchen Wang, Yanhao Fan, Ju Tan, Kian-Lee |
| Author_xml | – sequence: 1 givenname: Yuchen orcidid: 0000-0001-9646-291X surname: Li fullname: Li, Yuchen email: yuchenli@smu.edu.sg organization: School of Information Systems, Singapore Management University, Singapore – sequence: 2 givenname: Ju orcidid: 0000-0003-4729-9903 surname: Fan fullname: Fan, Ju email: fanj@ruc.edu.cn organization: Key Lab of Data Engineering and Knowledge Engineering, MOE of China, and the School of Information, Renmin University of China, Beijing, China – sequence: 3 givenname: Yanhao orcidid: 0000-0002-7661-3917 surname: Wang fullname: Wang, Yanhao email: yanhao90@comp.nus.edu.sg organization: School of Computing, National University of Singapore, Singapore – sequence: 4 givenname: Kian-Lee surname: Tan fullname: Tan, Kian-Lee email: tankl@comp.nus.edu.sg organization: School of Computing, National University of Singapore, Singapore |
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| SubjectTerms | algorithm design Algorithms Classification algorithms Computational modeling Diffusion processes Influence maximization information diffusion Information dissemination Integrated circuit modeling Maximization Social network services Social networks Stochastic processes Taxonomy |
| Title | Influence Maximization on Social Graphs: A Survey |
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