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Influence Minimization With Conformity-Aware Competitions
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AI
DOI：10.1109/JSYST.2022.3204751.png)
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Abstract
En 中文
Negative information (such as rumors that prevail) may produce tremendous damage in society incredibly quickly. One practical way to control negative information is to add positive information into the spread mechanism, which enjoys both effectiveness and cost-economic counterbalance. This article presents a novel conformity-aware competitive independent cascade model to simulate the spread process of two items (e.g., truth and rumor). Two advanced algorithms named the rumor-aware betweenness first (RBF) and conformity-aware demand degree (CDD) are proposed to solve the corresponding influence minimization problem. Extensive experiments conducted on seven synthetic/real-world networks have demonstrated that first, the proposed CDD or RBF algorithm outperforms other state-of-the-art methods in terms of rumor containment and efficiency in running speed; second, the time delay of rumor detection is entitled to be concerned about and costly measures are necessary if rumors have diffused chronically; and third, authoritative truth information plays a critical role in competing with rumors, relying on truth seeds will generate negative feedback when their credibility ratio is less than a specific threshold.
Keywords:
Competitive cascading
conformity
influence minimization
rumor blocking (RB)
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