Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98796
Title: How well-connected individuals help spread influences -- analyses based on preferential voter model
Authors: Lee, Zhuo Qi
Hsu, Wen-Jing
Lin, Miao
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Lee, Z. Q., Hsu, W. J.,& Lin, M. (2012). How Well-Connected Individuals Help Spread Influences -- Analyses Based on Preferential Voter Model. 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 674-678.
Abstract: The spread of influence in a network is a fundamental issue in many complex systems. For instance, the word of-mouth effect in a social network is crucial to the emerging viral market. The process was previously analyzed with a voter model where an individual weighs the opinions of his acquaintances equally. However, one would expect that an individual's opinion is often more swayed by her/his better connected peers. As such, the influence of a node x on another node should be proportional to x's degree. This paper studies the spread of influence under the latter model - called Preferential Voter Model (PVM) for easier reference. We first present the exact form of the random walk stationary distribution for PVM, based on which, we show that the First Passage Time (FPT) of PVM follows an exponential decay. Furthermore, compared against the conventional (nonpreferential) voter model, we show that nodes with larger degrees exhibit faster decay rate and lower mean First Passage Time in PVM. These new results have applications to network-based complex systems. For instance, in the context of viral marketing in a social network, picking nodes of larger degrees as the starting nodes of a sales campaign not only maximizes the expected number of influenced nodes in the network, but also reduces the expected time to spread the influence. Thus, our result confirms analytically that well-connected individuals indeed exert faster and more effective influences in social networks under the preferential model.
URI: https://hdl.handle.net/10356/98796
http://hdl.handle.net/10220/12684
DOI: http://dx.doi.org/10.1109/ASONAM.2012.112
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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