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238
STRIP: Stream Learning of Influence Probabilities
"... Influencedriven diffusion of information is a fundamental process in social networks. Learning the latent variables of such process, i.e., the influence strength along each link, is a central question towards understanding the structure and function of complex networks, modeling information cascade ..."
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Influencedriven diffusion of information is a fundamental process in social networks. Learning the latent variables of such process, i.e., the influence strength along each link, is a central question towards understanding the structure and function of complex networks, modeling information
Why Rumors Spread Fast in Social Networks
"... Understanding structural and algorithmic properties of complex networks is an important task, not least because of the huge impact of the internet. Our focus is to analyze how news spreads in social networks. We simulate a simple information spreading process in different network topologies and demo ..."
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Cited by 2 (1 self)
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Understanding structural and algorithmic properties of complex networks is an important task, not least because of the huge impact of the internet. Our focus is to analyze how news spreads in social networks. We simulate a simple information spreading process in different network topologies
CUTTINGEDGE: Influence Minimization in Networks
"... The diffusion of undesirable phenomena over social, information and technological networks is a common problem in different domains. Domain experts typically intervene to advise solutions that mitigate the spread in question. However, these solutions are temporary, in that they only tackle the sprea ..."
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the spread of a specific instance over the network, failing to account for the complex modalities of diffusion processes. We propose an optimization formulation for the problem of minimizing the spread of influence in a network by removing some of its edges. We show that the corresponding objective function
Network science Complex network Community detection
"... This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or sel ..."
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, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information
Indicators for Social and Economic Coping Capacity  Moving Toward a Working Definition of Adaptive Capacity”, WesleyanCMU Working Paper.
, 2001
"... Abstract This paper offers a practically motivated method for evaluating systems' abilities to handle external stress. The method is designed to assess the potential contributions of various adaptation options to improving systems' coping capacities by focusing attention directly on the u ..."
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Cited by 109 (14 self)
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, 6. The system's access to risk spreading processes, 7. The ability of decisionmakers to manage information, the processes by which these decisionmakers determine which information is credible, and the credibility of the decisionmakers, themselves, and 8. The public's perceived
RandomnessEfficient Rumor Spreading
, 1304
"... We study the classical rumor spreading problem, which is used to spread information in an unknown network with n nodes. We present the first protocol for any expander graph G with n nodes and minimum degree Θ(n) such that, the protocol informs every node in O(logn) rounds with high probability, and ..."
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We study the classical rumor spreading problem, which is used to spread information in an unknown network with n nodes. We present the first protocol for any expander graph G with n nodes and minimum degree Θ(n) such that, the protocol informs every node in O(logn) rounds with high probability
On Budgeted Influence Maximization in Social Networks
 IEEE Journal on Selected Areas in Communications
"... Abstract—Given a budget and arbitrary cost for selecting each node, the budgeted influence maximization (BIM) problem concerns selecting a set of seed nodes to disseminate some information that maximizes the total number of nodes influenced (termed as influence spread) in social networks at a total ..."
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Abstract—Given a budget and arbitrary cost for selecting each node, the budgeted influence maximization (BIM) problem concerns selecting a set of seed nodes to disseminate some information that maximizes the total number of nodes influenced (termed as influence spread) in social networks at a total
Quasirandom rumor spreading: An experimental analysis
 In Proceedings of the Workshop on Algorithm Engineering and Experiments (ALENEX
, 2009
"... We empirically analyze two versions of the wellknown “randomized rumor spreading ” protocol to disseminate a piece of information in networks. In the classical model, in each round each informed node informs a random neighbor. At SODA 2008, three of the authors proposed a quasirandom variant. Here, ..."
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Cited by 10 (6 self)
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We empirically analyze two versions of the wellknown “randomized rumor spreading ” protocol to disseminate a piece of information in networks. In the classical model, in each round each informed node informs a random neighbor. At SODA 2008, three of the authors proposed a quasirandom variant. Here
RESEARCH ARTICLE Immunization against the Spread of Rumors in Homogenous Networks
"... Since most rumors are harmful, how to control the spread of such rumors is important. In this paper, we studied the process of "immunization " against rumors by modeling the process of rumor spreading and changing the termination mechanism for the spread of rumors to make the model more r ..."
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an immunization threshold value that represents the minimum level required to stop the rumor from spreading. Numerical simulations revealed that the average degree of the network and parameters of transformation probability significantly influence the spread of rumors. More importantly, the simulations revealed
Dynamics of Large Networks
, 2008
"... A basic premise behind the study of large networks is that interaction leads to complex collective behavior. In our work we found very interesting and counterintuitive patterns for time evolving networks, which change some of the basic assumptions that were made in the past. We then develop models ..."
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Cited by 33 (0 self)
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graph structure and run simulations of network evolution. Another important aspect of our research is the study of “local ” patterns and structures of propagation in networks. We aim to identify building blocks of the networks and find the patterns of influence that these blocks have on information
Results 1  10
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