在线社交网络中的多主题谣言溯源OACSTPCD

With the rapid development of communication technology,information between users can flow quickly,which also leads to the spread of rumors in social networks,so there is an urgent need to detect the source of rumors to ensure the credibility of social networks.At present,the research on rumor traceability basically focuses on the spread of single-topic rumors.However,there are a large number of rumors with different topics in social networks.The more the source of rumors and the number of rumor topics,the greater the adverse effects.In view of the fact that multi-topic rumors exist at the same time,the process of information dissemination needs to be redefined.Therefore,a multi-topic independent cascade model is proposed,and the rumor traceability problem is defined on the basis of this model.From the infected network subgraph,the first k suspicious nodes are identified based on the principle of maximizing influence,and this group of nodes is considered to be the most likely source of rumors.It is proved that the problem is NP-hard and the objective function is monotone and submodular.On this basis,a greedy algorithm based on influence maximization with approximate ratio(1-1/e)is proposed.Experiments on large real data sets show that the average error distance is controlled within 1 hop.And compared with other algorithms,the proposed algorithm has higher accuracy and effectiveness.