The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various *** order to block the outbreak of rumor,one of the most effective containment measures is spreading p...
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The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various *** order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of *** spreading mechanism of rumors and effective suppression strategies are significant and challenging research ***,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same ***,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social *** crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed *** the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the *** results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social ***,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.
The rapid development of social networks has brought many conveniences, but it has also resulted in the wanton dissemination of negative information. Identifying key users in the network to block negative information ...
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The rapid development of social networks has brought many conveniences, but it has also resulted in the wanton dissemination of negative information. Identifying key users in the network to block negative information in a timely and effective manner has become an urgent research task. For this purpose, this paper proposes a binary ions motion optimization algorithm to maximize the blocking of negative influence propagation under a competitive-based model. The algorithm adopts a degree-based heuristic initialization strategy by recoding search agents and blocking diffusion channels based on the negative seed location. To overcome the lack of crystal phase search ability, a crossover mechanism of anions and cations is introduced, which accelerates convergence and facilitates the discovery of optimal solution. Finally, the effectiveness of the proposed algorithm is demonstrated on real networks and synthetic networks, showing significant advancements compared to other algorithms.
In the emerging landscape of online social networks (OSNs), the rapid dissemination of misinformation poses a significant challenge to the integrity of information shared among users. Hence, misinformation containment...
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In the emerging landscape of online social networks (OSNs), the rapid dissemination of misinformation poses a significant challenge to the integrity of information shared among users. Hence, misinformation containment problem in OSNs has drawn significant attention nowadays. In this paper, given a fixed budget, the problem is formulated as minimizing misinformation spread (MMS) problem, which is shown to be an NP-hard problem. With the objective to combat the misinformation in real time, this paper explores a new direction to leverage the network topology to minimize the search space drastically. Based on the community structure of the OSN along with the trust relationship among nodes, a novel linear-time seed node selection algorithm is proposed here that is independent of the positions of the misinformed nodes. Once the set of seed nodes is selected, it can combat any situation of misinformation spread in the OSN, provided the community structure of the network does not change significantly. To the best of our knowledge, this work is the first where trust relationship among users is considered along with the community structure of the network, to control the spread of misinformation in real time. To analyze the diffusion dynamics pertaining to both true information and misinformation, competitive linear threshold model (LTM) with provision for belief switching is followed to provide a more realistic and comprehensive understanding of information diffusion dynamics. Extensive experimental studies on large scale OSNs demonstrate that in comparison to earlier works, the proposed technique obtains 47-74% improvements in performance parameters. Not only that, its parallel implementations also achieve around 51x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$51\times$$\end{document} speedup
In today's world, Online Social Networks (OSNs) play a crucial role in our everyday life. But, its abuse to disseminate misinformation has turned out to be a major concern to us. Hence, the misinformation containm...
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ISBN:
(纸本)9781665477062
In today's world, Online Social Networks (OSNs) play a crucial role in our everyday life. But, its abuse to disseminate misinformation has turned out to be a major concern to us. Hence, the misinformation containment (MC) problem has attracted a lot of attention in recent times. For a given OSN with a fixed budget, this paper proposes a trust-based static technique independent of the distribution of misinformed nodes to select a set of trusted seed nodes leveraging the topologies of the network, to contain and decimate the misinformation faster. We follow a modified form of competitive linear threshold model with One Direction state Transition (LT1DT) to study the propagation dynamics of both the correct information and misinformation. Simulation studies on three real-world OSNs show that proposed method outperforms earlier work [1] significantly in terms of maximum number of misinformed nodes, infected time, point of inflection and number of misinformed nodes in steady state respectively. Moreover, its parallel implementation achieves almost 32x speedup, making the procedure scalable for large scale OSNs to contain and decimate misinformation in real-time.
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