Abstract—With the rapid development of social media
technology, many different pieces of information, ideas,
products, and innovations are propagating widely in online
social networks. Information diffusion has been further
researched by many scientists and experts in the past few
decades. In this paper, we study the competitive influence
propagation in sina microblog under the competitive
independent cascade model, which extends the classical
independent cascade model. This paper pays attention to the
problem that adaptively selecting a certain number of seeds to
maximize its influence benefit (IB) under a competitive diffusion
model. We call this problem the adaptive influence
maximization (AIM) problem. The traditional Monte-Carlo
greedy algorithm can select a specified number of seeds, and has
a very high complexity. A new efficient algorithm called
M-based algorithm is designed to select seeds adaptively, and
faster than the Monte-Carlo greedy algorithm.
Index Terms—Adaptive influence maximization, competitive
independent cascade model, microblog.
The authors are with the Key Laboratory of Universal Wireless
Communication, Ministry of Education, Beijing University of Posts and
Telecommunications, Beijing 100876, China (e-mail: dzsd2013@163.com,
niukai@bupt.edu.cn, hezq@bupt.edu.cn).
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Cite: Zheng Ding, Kai Niu, and Zhiqiang He, "Adaptive Influence Maximization in Microblog under the Competitive Independent Cascade Model," International Journal of Knowledge Engineering vol. 1, no. 2, pp. 129-133, 2015.
