Proposal Type

Event

Presenter Information

mingyuan yanFollow

Keywords

Social Networks, Influence Maximization, Algorithm, Data Mining

Subject Area

Computer Science/GIS

Description/Abstract

The goal of classic influence maximization in Online Social Networks (OSNs) is to maximize the spread of influence with a fix budget constraint, e.g. the size of seed nodes is pre-determined. However, most existing works on influence maximization overlooked the information timeliness. That is, these works assume the influence will not decay with time and the influence could be accepted immediately, which are not practical. Secondly, even the influence could be passed to a special node in time, whether the influence could be delivered (influence take effect) or not is still an unknown question. Furthermore, if let the number of users who are influenced as the depth of influence and the area covered by influenced users as the breadth, most of research results are only focus on the influence depth instead of the influence breadth. Timeliness, acceptance ratio and breadth are three important factors neglected but strong affect the real result of influence maximization. In order to fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio and broad diffusion for influence breadth has been investigated in this paper. In our model, the breadth of influence is measured by the number of communities, and the tradeoff between depth and breadth of influence could be balanced by a parameter φ. Empirical studies on different large real-world social networks show that our model demonstrates that high depth influence does not necessarily imply broad information diffusion. Our model, together with its solutions, not only provides better practicality but also gives a regulatory mechanism for influence maximization as well as outperforms most of the existing classical algorithms.

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.

Rights

This work is still under review, please do not open it to public.

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An Exploration of Broader Influence Maximization in Timeliness Networks with Opportunistic Selection

The goal of classic influence maximization in Online Social Networks (OSNs) is to maximize the spread of influence with a fix budget constraint, e.g. the size of seed nodes is pre-determined. However, most existing works on influence maximization overlooked the information timeliness. That is, these works assume the influence will not decay with time and the influence could be accepted immediately, which are not practical. Secondly, even the influence could be passed to a special node in time, whether the influence could be delivered (influence take effect) or not is still an unknown question. Furthermore, if let the number of users who are influenced as the depth of influence and the area covered by influenced users as the breadth, most of research results are only focus on the influence depth instead of the influence breadth. Timeliness, acceptance ratio and breadth are three important factors neglected but strong affect the real result of influence maximization. In order to fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio and broad diffusion for influence breadth has been investigated in this paper. In our model, the breadth of influence is measured by the number of communities, and the tradeoff between depth and breadth of influence could be balanced by a parameter φ. Empirical studies on different large real-world social networks show that our model demonstrates that high depth influence does not necessarily imply broad information diffusion. Our model, together with its solutions, not only provides better practicality but also gives a regulatory mechanism for influence maximization as well as outperforms most of the existing classical algorithms.