Abstract
With the advancement of science and technology, research on influence maximization in networks has become a hot spot. In social networks, there is not only a positive relationship among nodes but also a negative impact, and the negative impact often plays a greater role than the positive impact, as observed for shopping websites or online votes. This paper proposes a method based on an independent cascade model by emphasizing the negative impact in symbolic networks to solve the problem of influence maximization. First, an algorithm that is based on an independent path and that emphasizes negative influence is designed to obtain the probability among nodes. Based on the activation probability, an algorithm is proposed to identify nodes that could have the greatest impact on the influence increment from the seeds. Finally, the seed set is confirmed based on the influence in the corresponding symbol network. In the experiment performed on real-world network data, the result indicate that the proposed algorithm causes more substantial influence propagation than do other algorithms.
Highlights
With the increasing penetration of network technology, such as Google and WeChat, in daily life [1], many researchers have collected data on such networks and launched a series of studies on network structure analysis [2], community detection [3], link prediction [4], etc
Before introducing the framework of the EN-influence maximization (IM) algorithm, we introduce the variables used in the algorithm: p (v, u): activation probability pq(v,u |V − S ) based on seed node S, which is initially set to pr(v,u); when S
EXPERIMENTAL RESULTS To verify the performance of the EN-IM algorithm, tests were conducted on three real-world data sets
Summary
With the increasing penetration of network technology, such as Google and WeChat, in daily life [1], many researchers have collected data on such networks and launched a series of studies on network structure analysis [2], community detection [3], link prediction [4], etc. Our work in this paper is based on the independent cascading model to emphasize the negative influence in signed networks. Several seed nodes are selected and set to the active state, and research on influence maximization is performed based on these nodes. Most studies on the influence maximization problem consider the spread of influence only from a positive perspective; that is, the relationship between network nodes is regarded as cooperative or friendly [1518]. In the IC model, evaluating the influence spreading of a given seed set involves a #P problem This time-consuming simulation process makes applying these methods in solving practical problems difficult. The research steps of this paper are as follows: (1) The problem of maximizing influence by emphasizing negative influence in social networks is formally defined. The research of this paper can help people to identify the positive and negative influence at the same time and provide a strong theoretical basis for them to make a reasonable choice
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