Centrality Measures, Upper Bound, and Influence Maximization in Large Scale Directed Social Networks
نویسندگان
چکیده
منابع مشابه
Centrality Measures, Upper Bound, and Influence Maximization in Large Scale Directed Social Networks
The paper addresses the problem of finding top k influential nodes in large scale directed social networks. We propose two new centrality measures, Diffusion Degree for independent cascade model of information diffusion and Maximum Influence Degree. Unlike other existing centrality measures, diffusion degree considers neighbors’ contributions in addition to the degree of a node. The measure als...
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متن کاملErrata to “Scalable influence maximization for independent cascade model in large-scale social networks”
1 The issue with the Prefix excluding MIA model In Section 3.3 of [1], we define the Prefix excluding MIA (PMIA) model. In particular, after several seeds are selected, we compute a maximum influence path from node u to node v avoiding all previously selected seeds. One can then define the influence spread function σP (·) based on the model, as in Equation (3.2) in [1]. We claimed that σP (·) i...
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Influence maximization problem is a well known problem to find the top-k seed users who can maximize the spread of information in a social network. The primary concern is monte carlo simulations method is suffering with scalability issues while the selection of seed users .It takes days to find potential seed users in large datasets. In this paper, we propose a highly scalable algorithm for ide...
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ژورنال
عنوان ژورنال: Fundamenta Informaticae
سال: 2014
ISSN: 0169-2968
DOI: 10.3233/fi-2014-994