Research On Minimization Of Dynamic Rumor Based On Community Discovery

With the rapid development of Internet technology,social networks with open features provide a broader platform for information dissemination.While information dissemination provides convenience for life,it also brings many problems such as security risks and public opinion orientation.For example,various bad,malicious,and false information spread across regions,seriously affecting social harmony and national security.Therefore,negative rumors need to be discovered and effectively prevented,minimize their spread and influence,and ensure a positive,stable and harmonious social order.Rumor blocking is a key research issue in social network analysis,with Important theoretical and practical value.At present,the problem of minimizing the impact of negative information such as online rumors has been widely concerned.The existing rumor blocking algorithm has prevented the spread of rumors to a certain extent,but the blocking effect of these algorithms needs to be improved.Community structure is a common phenomenon in social networks.The connections between nodes in the same community are relatively dense,and the connections between nodes in different communities are relatively sparse.The community structure not only reflects the characteristics of individual nodes in the network and the associated information between the communities,but also simplifies the entire network into hierarchical and characteristic community and community relationships.Using the community structure to analyze the impact problem,on the one hand,helps to reduce the complexity of the entire social network research,on the other hand,it helps the impact assessment,and can simultaneously consider the relationship between the nodes within the community and the important relationship between the communities.Therefore,the shortcomings of the existing influence minimization algorithm can be overcome.This paper focuses on the minimization of the impact of negative information such as online rumors,and proposes a dynamic rumor impact minimization(CoF-DRIM)method based on community structure.The method first divides the global network into communities.The nodes in the community are closely related,and the nodes between the communities are relatively sparse.Secondly,this paper measures the influence of the nodes in the community and the probability of the nodes being infected.The influence of the nodes,and then effectively dig out the key nodes in each community,and consider the bridge nodes connecting the communities,the two merge into a blocking node,and then use the dynamic blocking algorithm to block the node,thereby reducing the entire social network.The rumor infection rate in the paper;Finally,this paper uses two real data sets to verify the proposed method.From the two aspects of infection rate and sensitivity,the algorithm is compared with other algorithms.Experimental results show that the proposed algorithm has better rumor blocking effect.