Immunization of complex networks using stochastic hill-climbing algorithm

Shams, Bita; Khansari, Mohammad

doi:10.1109/iccke.2013.6682858

Cited by 11 publications

(11 citation statements)

References 30 publications

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“…The PageRank immunization (PI), selects individuals based on their Google PageRank, which determines the probability of visiting a node in a random walk (Page et al, 1999;Ventresca and Aleman, 2013). PageRank immunization is proposed based on the idea that nodes with high PageRank centrality are more likely to be infected or infect others along many paths.…”

Section: Pagerank Immunizationmentioning

confidence: 99%

“…fragmenting networks such that the size of largest connected component is minimized (Chen et al, 2008;Schneider et al, 2012Schneider et al, , 2011Shams and Khansari, 2013). The basic assumption of these strategies is that an infected node can maximally infect nodes that are in its containing component.…”

Section: Stochastic Hill-climbing Immunizationmentioning

confidence: 99%

“…Stochastic hill-climbing immunization (SHCI) is the newest immunization strategy in this category which is designed to immunize networks with a certain amount of vaccination units (Shams and Khansari, 2013). This algorithm employs the Stochastic hill-climbing method to find the specified size subset of nodes whose removal minimizes largest connected component (LCC) of the network.…”

Section: Stochastic Hill-climbing Immunizationmentioning

confidence: 99%

“…Recently, several other immunization approaches have been put forward that focus on reducing the size of the largest connected component (i.e. set of vertices that are reachable from each other) of the network of non-immunized nodes in order to reduce worst-case epidemic size (Schneider et al, 2012(Schneider et al, , 2011Shams and Khansari, 2013).…”

Section: Introductionmentioning

confidence: 99%

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On the impact of epidemic severity on network immunization algorithms

Shams

Khansari

2015

Theoretical Population Biology

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There has been much recent interest in the prevention and mitigation of epidemics spreading through contact networks of host populations. Here, we investigate how the severity of epidemics, measured by its infection rate, influences the efficiency of well-known vaccination strategies. In order to assess the impact of severity, we simulate the SIR model at different infection rates on various real and model immunized networks. An extensive analysis of our simulation results reveals that immunization algorithms, which efficiently reduce the nodes' average degree, are more effective in the mitigation of weak and slow epidemics, whereas vaccination strategies that fragment networks to small components, are more successful in suppressing severe epidemics.

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Section: Pagerank Immunizationmentioning

confidence: 99%

Section: Stochastic Hill-climbing Immunizationmentioning

confidence: 99%

Section: Stochastic Hill-climbing Immunizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the impact of epidemic severity on network immunization algorithms

Shams

Khansari

2015

Theoretical Population Biology

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“…According to the results, both for average infection probability and average time to stabilize, the multi-objective key players identified on 60 % sample of the original network achieve the best result among all key player identification algorithms for 80 % of the cases. It should be noted that more sophisticated approaches have been proposed to address the problem of maximizing information diffusion in social networks (Kang et al 2012;Broecheler et al 2010) and the problem of immunization (Shams and Khansari 2013;Schneider et al 2011). But these approaches require more information about the network (such as node, edge attributes) and are proposed to solve these specific problems.…”

Section: The Average Infection Probability Of All the Nodes In The Nementioning

confidence: 99%

Multi-objective optimization to identify key players in large social networks

Gunasekara

Mehrotra

Mohan

2015

Soc. Netw. Anal. Min.

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Identification of a set of key players in a given social network is of interest in many disciplines such as sociology, politics, finance, economics, etc. Although many algorithms have been proposed to identify a set of key players, each emphasizes a single objective of their interest. Consequently, the prevailing deficiency of each of these methods is that they perform well only when we consider their objective of interest as the only characteristic the set of key players should have. But in complicated real life applications, we need a set of key players which can perform well with respect to multiple objectives of interest. In this paper, we propose a new perspective for key player identification, based on optimizing multiple objectives of interest. This method allows us to compare other methods of key player identification. The sets of key players identified by this method are better when multiple objectives must be addressed. In addition we propose an algorithm to select the most suitable sets of key players when multiple choices are available. To reduce the computational complexity of the proposed approach for large networks, we propose a new sampling approach based on Degree centrality. We apply these algorithms in eventual influence limitation (EIL) problem and immunization problem and show that our multi-objective methodology outperforms previous key player identification approaches.

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Graph-based collaborative ranking

Shams

Haratizadeh

2017

Expert Systems with Applications

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Data sparsity, that is a common problem in neighbor-based collaborative filtering domain, usually complicates the process of item recommendation. This problem is more serious in collaborative ranking domain, in which calculating the users' similarities and recommending items are based on ranking data. Some graph-based approaches have been proposed to address the data sparsity problem, but they suffer from two flaws. First, they fail to correctly model the users' priorities, and second, they can't be used when the only available data is a set of ranking instead of rating values.In this paper, we propose a novel graph-based approach, called GRank, that is designed for collaborative ranking domain. GRank can correctly model users' priorities in a new tripartite graph structure, and analyze it to directly infer a recommendation list. The experimental results show a significant improvement in recommendation quality compared to the state of the art graph-based recommendation algorithms and other collaborative ranking techniques.

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Immunization of complex networks using stochastic hill-climbing algorithm

Cited by 11 publications

References 30 publications

On the impact of epidemic severity on network immunization algorithms

On the impact of epidemic severity on network immunization algorithms

Multi-objective optimization to identify key players in large social networks

Graph-based collaborative ranking

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