Community-diversity Driven Influence Maximization on Social Networks
Article
Li, Jianxin, Cai, Taotao, Ke, Deng, Wang, Xinjue, Sellis, Timos and Xia, Feng. 2020. "Community-diversity Driven Influence Maximization on Social Networks." Information Systems. 92. https://doi.org/10.1016/j.is.2020.101522
| Article Title | Community-diversity Driven Influence Maximization on Social Networks |
|---|---|
| ERA Journal ID | 39628 |
| Article Category | Article |
| Authors | Li, Jianxin, Cai, Taotao, Ke, Deng, Wang, Xinjue, Sellis, Timos and Xia, Feng |
| Journal Title | Information Systems |
| Journal Citation | 92 |
| Article Number | 101522 |
| Number of Pages | 12 |
| Year | Sep 2020 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0306-4379 |
| 1873-6076 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.is.2020.101522 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0306437920300326 |
| Abstract | To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. |
| Keywords | Social community; Influence maximization; Diversified influence propagation |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
| Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
| Byline Affiliations | Deakin University |
| Royal Melbourne Institute of Technology (RMIT) | |
| Swinburne University of Technology | |
| Federation University |
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