CIM

Categorical influence maximization

Siyuan Liu, Lei Chen, Lionel M. Ni, Jianping Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Influence maximization is an interesting and well-motivated problem in social networks study. The traditional influence maximization problem is defined as finding the most "influential" vertices without considering the vertex attribute. Though it is useful, in practice, there exist different attributes for vertices, e.g., mobile phone social networks. So, it is more important and useful to capture the vertices having the maximum influence in different search categories, which is exactly the problem that we study in this work. Thus, we name this new problem as Categorical Influence Maximization (CIM). Compare with identifying maximum influence vertices in a single category social network, CIM is much harder because we have to deal with large scale complex data. In this work, based on the observations from real mobile phone social network data, we propose a Probability Distribution based Search method (PDS) to tackle the CIM problem. Specifically, the PDS method consists of three steps. First, we propose a probability distribution based parameter free method (PD-max) to identify the maximum influential vertex set for the specified category by studying the categorical influential distribution within a time interval. Second, among these detected influential vertices, we design a probability distribution based minimizing method (PD-minmax) to find the minimum number of vertices in each category having the maximum influences. We test our solutions with real data sets, which were collected for one year in a city in China. The extensive experiment results show that our methods outperform the existing ones.

Original languageEnglish (US)
Title of host publicationProceedings of the 5th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2011
DOIs
StatePublished - May 20 2011
Event5th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2011 - Seoul, Korea, Republic of
Duration: Feb 21 2011Feb 23 2011

Other

Other5th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2011
CountryKorea, Republic of
CitySeoul
Period2/21/112/23/11

Fingerprint

Probability distributions
Mobile phones
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

Cite this

Liu, S., Chen, L., Ni, L. M., & Fan, J. (2011). CIM: Categorical influence maximization. In Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2011 [124] https://doi.org/10.1145/1968613.1968757
Liu, Siyuan ; Chen, Lei ; Ni, Lionel M. ; Fan, Jianping. / CIM : Categorical influence maximization. Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2011. 2011.
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Liu, S, Chen, L, Ni, LM & Fan, J 2011, CIM: Categorical influence maximization. in Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2011., 124, 5th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2011, Seoul, Korea, Republic of, 2/21/11. https://doi.org/10.1145/1968613.1968757

CIM : Categorical influence maximization. / Liu, Siyuan; Chen, Lei; Ni, Lionel M.; Fan, Jianping.

Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2011. 2011. 124.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Influence maximization is an interesting and well-motivated problem in social networks study. The traditional influence maximization problem is defined as finding the most "influential" vertices without considering the vertex attribute. Though it is useful, in practice, there exist different attributes for vertices, e.g., mobile phone social networks. So, it is more important and useful to capture the vertices having the maximum influence in different search categories, which is exactly the problem that we study in this work. Thus, we name this new problem as Categorical Influence Maximization (CIM). Compare with identifying maximum influence vertices in a single category social network, CIM is much harder because we have to deal with large scale complex data. In this work, based on the observations from real mobile phone social network data, we propose a Probability Distribution based Search method (PDS) to tackle the CIM problem. Specifically, the PDS method consists of three steps. First, we propose a probability distribution based parameter free method (PD-max) to identify the maximum influential vertex set for the specified category by studying the categorical influential distribution within a time interval. Second, among these detected influential vertices, we design a probability distribution based minimizing method (PD-minmax) to find the minimum number of vertices in each category having the maximum influences. We test our solutions with real data sets, which were collected for one year in a city in China. The extensive experiment results show that our methods outperform the existing ones.

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Liu S, Chen L, Ni LM, Fan J. CIM: Categorical influence maximization. In Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2011. 2011. 124 https://doi.org/10.1145/1968613.1968757