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@article{145005,
        author = {Ravikiran Batchalakuri and Ganesh Konda},
        title = {Revelation and Minimization of Rumor Impact in Shared Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {6},
        pages = {377-381},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145005},
        abstract = {With the fast development of big scale on-line social networks, on-line data sharing is becoming omnipresent daily. numerous info is propagating through on-line social networks similarly as every the positive and negative. Throughout this paper, we tend to tend to focus on the negative data problems just like the on-line rumors. Rumor block may well be a significant drawback in large-scale social networks. Malicious rumors might cause chaos in society and soought to be blocked as soon as potential once being detected. during this paper, we tend to propose a model of dynamic rumor influence reduction with user expertise (DRIMUX).Our goal is to cut back the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by block an exact set of nodes. A dynamic Ising propagation model considering every the worldwide quality and individual attraction of the rumor is given supported realistic state of affairs. to boot, altogether completely different from existing problems with influence reduction, we tend to tend to require into thought the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the block time of each user exceeds that threshold, the utility of the network will decrease. underneath this constraint, we tend to tend to then formulate draw back as a network abstract thought drawback with survival theory, and propose solutions supported most probability principle. Experiments area unit implemented supported large-scale world networks and validate the effectiveness of our methodology.},
        keywords = {Rumor Influence, Social Network},
        month = {},
        }