A Max–Min Ant System With Repetitive Influence Reduction Strategy for Interactive Dissemination of Positive and Negative Information
Xuan-Li Shi, Wei–Neng Chen, Jinghui Zhong, Jun Zhang
Abstract
The rapid development of online social networks (OSNs) has facilitated people to express opinions and share information. To optimize the utility of information dissemination in OSNs, problems such as influence maximization have received increasing attention in recent years. However, not only positive information but also negative information is spreading in OSNs. The dissemination of positive and negative information interacts with each other, making network dissemination analysis and utility optimization more challenging. To this end, we develop a negative–neutral–positive–susceptible (NNPS) model and propose a max–min ant system algorithm with a repetitive influence reduction strategy (MMAS-RIR). First, an NNPS model with a novel heterogenous influence indicator is constructed to simulate the interactive dissemination of positive and negative information. The influence of each user’s neighbors on each user is treated differently, producing heterogenous state transition probabilities for users. Second, we formulate the control of information dissemination as an optimization problem with a designed control scheme. The disruption strategy and counterbalance strategy are automatically implemented on the selected users according to their states in the control scheme. Third, we specially develop a MMAS-RIR algorithm for the formulated problem, where the repetitive influence reduction strategy is used to reduce the influence repeated range of the connected users. Moreover, to improve the exploitation, an adaptive local search is added in MMAS-RIR. Finally, various experiments are conducted to validate the effectiveness of our work.