Competitive Influence Maximization on Online Social Networks under Cost Constraint

• Bo-Lun Chen,
• Yi-Yun Sheng,
• Min Ji,
• Ji-Wei Liu,
• Yong-Tao Yu,
• Yue Zhang,

Vol. 15, No. 4, April 30, 2021
10.3837/tiis.2021.04.005, Download Paper (Free):

• Competitive Influence Maximization
• Transition Probability
• Cost Constraint
• Social Networks

Abstract

In online competitive social networks, each user can be influenced by different competing influencers and consequently chooses different products. But their interest may change over time and may have swings between different products. The existing influence spreading models seldom take into account the time-related shifts. This paper proposes a minimum cost influence maximization algorithm based on the competitive transition probability. In the model, we set a one-dimensional vector for each node to record the probability that the node chooses each different competing influencer. In the process of propagation, the influence maximization on Competitive Linear Threshold (IMCLT) spreading model is proposed. This model does not determine by which competing influencer the node is activated, but sets different weights for all competing influencers. In the process of spreading, we select the seed nodes according to the cost function of each node, and evaluate the final influence based on the competitive transition probability. Experiments on different datasets show that the proposed minimum cost competitive influence maximization algorithm based on IMCLT spreading model has excellent performance compared with other methods, and the computational performance of the method is also reasonable.

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