Abstract
Social networks have become a powerful information spreading platform. How to limit rumor spread on social networks is a challenging problem. In this article, we combine information spreading mechanisms to simulate real-world social network user behavior. Based on this, we estimate the risk degree of each node during the hazard period and analyze the hazard level that other nodes are potentially affected by when a node is infected by a rumor. We use the Rumor Path Tree (RPT) to analyze the rumor spreading path. By comparing the rumors and truths propagation to a certain node, the steps taken by the rumor node to propagation are estimated. In order to identify the truth node, we construct a fractional function to calculate the effective influence nodes, and select the node with the highest score from the generated RPT pool. Based on the truth node we effectively block the spread of rumors. Finally, experimental results and comparisons on the real datasets prove that our method is effective and efficient.
Highlights
Social networks provide users with a new way to spread messages
Aiming at the problem of rumor propagation in social networks, we construct a multi-level propagation model based on entropy weight
By analyzing the propagation path of the rumor, we use the specific node as the root of the rumor path tree structure in the active period of the rumor
Summary
Social networks provide users with a new way to spread messages. Users can share recent updates, recommended music and videos via social networks. Due to the high openness and spread of message transmission, the network is full of false and even harmful rumors. Limiting the spread of rumors and minimizing their influence have become the challenging problem. Nodes U2, U3, U4 are becoming recipients. After accepting the rumor by U1, node U2 is the receiver, and the initiator. That, U2 passes the rumor to U3 and U4 again.
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