Title

Influence Maximization by Probing Partial Communities in Dynamic Social Networks

Proposal Type

Poster

Presenter Information

mingyuan yanFollow

Keywords

Influence Maximization, community detection, social network

Subject Area

Computer Science/GIS

Start Date

11-11-2016 11:45 AM

End Date

11-11-2016 1:15 PM

Description/Abstract

With the rapid development of online social networks, exploring influence maximization for product publicity and advertisement marketing has attracted strong interests from both the academia and industry. However, due to the continuous network topology changing, updating the variation of an entire network moment by moment is resource intensive and often insurmountable. On the other hand, the classical influence maximization models IC and LT together with their derived varieties are all computationally intensive. Thus developing a solution for dynamic networks with lower cost and higher accuracy is in an urgent necessity. In this paper, a practical framework is proposed by only probing partial communities to explore the real changes of a network. Our framework minimizes the possible difference between the observed topology and the real network through several representative communities. Based on the framework, an algorithm that takes full advantage of divide-and-conquer strategy which reduces the computational overhead is proposed. The systemically theoretical analysis shows that the proposed effective algorithm could achieve provable approximation guarantees. Empirical studies on synthetic and real large scale social networks demonstrate that our framework has better practicality compared with most existing works and provides a regulatory mechanism for enhancing influence maximization.

Bio

Dr. Mingyuan Yan is an Assistant Professor in Computer Science at University of North Georgia. She received her Ph.D. degree in 2015 from Department of Computer Science at Georgia State University. She received her B.S. in Computer Science and Technology and M.S. in Information Security from Wuhan University, Wuhan, China, in 2008 and 2010, respectively. In 2012, she received another M.S. Degree in Computer Science from Georgia State University. Her research interests include data management and protocol design in wireless networks, influence maximization and information dissemination in mobile social networks. She is also interested in other topics such as information security and big data management. Dr. Yan is an IEEE member, and an IEEE COMSOC member.

 
Nov 11th, 11:45 AM Nov 11th, 1:15 PM

Influence Maximization by Probing Partial Communities in Dynamic Social Networks

With the rapid development of online social networks, exploring influence maximization for product publicity and advertisement marketing has attracted strong interests from both the academia and industry. However, due to the continuous network topology changing, updating the variation of an entire network moment by moment is resource intensive and often insurmountable. On the other hand, the classical influence maximization models IC and LT together with their derived varieties are all computationally intensive. Thus developing a solution for dynamic networks with lower cost and higher accuracy is in an urgent necessity. In this paper, a practical framework is proposed by only probing partial communities to explore the real changes of a network. Our framework minimizes the possible difference between the observed topology and the real network through several representative communities. Based on the framework, an algorithm that takes full advantage of divide-and-conquer strategy which reduces the computational overhead is proposed. The systemically theoretical analysis shows that the proposed effective algorithm could achieve provable approximation guarantees. Empirical studies on synthetic and real large scale social networks demonstrate that our framework has better practicality compared with most existing works and provides a regulatory mechanism for enhancing influence maximization.