NDLI: Competition between two sets of agents on complex networks

Content Provider	IEEE Xplore Digital Library
Author	Jiuhua Zhao Qipeng Liu Lin Wang Xiaofan Wang
Copyright Year	2015
Description	Author affiliation: Inst. of Complexity Sci., Qingdao Univ., Qingdao, China (Qipeng Liu) \|\| Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai, China (Jiuhua Zhao; Lin Wang; Xiaofan Wang)
Abstract	In this paper, we consider the competitive dynamics on complex networks where two sets of agents have fixed and opposite-signed states, called competitors, and other agents, called normal agents, update their states based on the influence of their neighbors. The competition outcome is defined in terms of the number of normal agents with specific sign. We introduce a criterion based on the Influence Matrix (IM), which is constructed according to the structure of the network, to predict the bias of each normal agent and furthermore predict which set of competitors will win the competition. We also compare the performance of IM criterion with several classic centrality-based criteria in predicting the competition outcome. Simulations show that, in the aspect of predicting the bias of a normal agent, the average success rate of IM criterion keeps high as the cardinality of set of competitors varies. In predicting the result of the competition, for undirected networks, IM criterion performs better than other centrality based criteria when the cardinality of the set of competitors is greater than one. For directed networks, IM criterion is also one of the best criteria.
Starting Page	1340
Ending Page	1344
File Size	229410
Page Count	5
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7259828
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Protocols Blogs Complex networks Predictive models Centrality Measures Control systems Influence Matrix Steady-state Electronic mail Competitive Dynamics
Content Type	Text
Resource Type	Article

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