Bridgeness: a local index on edge significance in maintaining global connectivity

Cheng, Xueqi; Ren, Fuxin; Shen, Huawei; Zhang, Zi-Ke; Zhou, Tao

doi:10.1088/1742-5468/2010/10/p10011

Cited by 91 publications

(79 citation statements)

References 38 publications

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“…We implemented all of these centrality measures but did not include the results because their predictions were usually very close to the predictions of the measures presented. An alternative intervention strategy is also to target the links between nodes rather than the nodes themselves (Girvan and Newman, 2002;Holme et al, 2002;Cheng et al, 2010;Enns et al, 2012;Marcelino and Kaiser, 2009;Omić et al, 2010;Restrepo et al, 2006). ECA methodology could be applied to the identification of the key links in the network by replacing the systematic removal of nodes with the systematic removal of links.…”

Section: Discussionmentioning

confidence: 99%

Epidemic control analysis: Designing targeted intervention strategies against epidemics propagated on contact networks

Hadjichrysanthou

Sharkey

2015

Journal of Theoretical Biology

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In cases where there are limited resources for the eradication of an epidemic, or where we seek to minimise possible adverse impacts of interventions, it is essential to optimise the efficacy of control measures. We introduce a new approach, Epidemic Control Analysis (ECA), to design effective targeted intervention strategies to mitigate and control the propagation of infections across heterogeneous contact networks. We exemplify this methodology in the context of a newly developed individual-level deterministic Susceptible-Infectious-Susceptible (SIS) epidemiological model (we also briefly consider applications to Susceptible-Infectious-Removed (SIR) dynamics). This provides a flexible way to systematically determine the impact of interventions on endemic infections in the population. Individuals are ranked based on their influence on the level of infectivity. The highest-ranked individuals are prioritised for targeted intervention. Many previous intervention strategies have determined prioritisation based mainly on the position of individuals in the network, described by various local and global network centrality measures, and their chance of being infectious. Comparisons of the predictions of the proposed strategy with those of widely used targeted intervention programmes on various model and real-world networks reveal its efficiency and accuracy. It is demonstrated that targeting central individuals or individuals that have high infection probability is not always the best strategy. The importance of individuals is not determined by network structure alone, but can be highly dependent on the infection dynamics. This interplay between network structure and infection dynamics is effectively captured by ECA.

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Section: Discussionmentioning

confidence: 99%

Epidemic control analysis: Designing targeted intervention strategies against epidemics propagated on contact networks

Hadjichrysanthou

Sharkey

2015

Journal of Theoretical Biology

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“…On the other hand, the weak ties phenomenon shows that the less similar individuals are prone to be connected with weaker strength. It has been proved that the weak ties phenomenon exists in the mobile communication [34] and document networks [35]. But, the weak ties effects are less studied for the PPI network.…”

Section: Weak Ties Phenomenon In Ppi Networkmentioning

confidence: 99%

Detecting protein complexes in PPI networks: The roles of interactions

Gao

2011

2011 IEEE International Conference on Systems Biology (ISB)

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Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in protein complexes. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, fail to take into account the inherence organization within protein complex and the roles of edges. To investigate the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. By using the concept of bridgeness, a reliable virtual network is constructed, in which each maximal clique corresponds to a core. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. Finally, a comprehensive comparison between the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. The experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms and analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, implying that the role of interactions is a critical and promising factor in extracting protein complexes.

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“…Tie strength usually plays an vital role in many real networks and is crucial to understand dynamical processes on the networks [6,7]. Weak ties display an important bridging function [8,9], while strong ties are more likely to activate the flow of referral information and more influential than weak ties [10,11]. In addition, weak ties could play a more significant role than strong ties to keep the stability [12], maintain the connectivity [13] and uncover the missing information [14,15], while strong ties can be better utilized to enhance the human resource flexibility [16], to * zhutou@ustc.edu provide accurate recommendation [17][18][19], and so on.…”

Section: Introductionmentioning

confidence: 99%

Strong ties promote the epidemic prevalence in susceptible–infected–susceptible spreading dynamics

Cui

Yang

Zhou

2016

Physica A: Statistical Mechanics and its Applications

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Understanding spreading dynamics will benefit society as a whole in better preventing and controlling diseases, as well as facilitating the socially responsible information while depressing destructive rumors. In network-based spreading dynamics, edges with different weights may play far different roles: a friend from afar usually brings novel stories, and an intimate relationship is highly risky for a flu epidemic. In this article, we propose a weighted susceptible-infected-susceptible model on complex networks, where the weight of an edge is defined by the topological proximity of the two associated nodes. Each infected individual is allowed to select limited number of neighbors to contact, and a tunable parameter is introduced to control the preference to contact through high-weight or low-weight edges. Experimental results on six real networks show that the epidemic prevalence can be largely promoted when strong ties are favored in the spreading process. By comparing with two statistical null models respectively with randomized topology and randomly redistributed weights, we show that the distribution pattern of weights, rather than the topology, mainly contributes to the experimental observations. Further analysis suggests that the weight-weight correlation strongly affects the results: high-weight edges are more significant in keeping high epidemic prevalence when the weight-weight correlation is present.

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Bridgeness: a local index on edge significance in maintaining global connectivity

Cited by 91 publications

References 38 publications

Epidemic control analysis: Designing targeted intervention strategies against epidemics propagated on contact networks

Epidemic control analysis: Designing targeted intervention strategies against epidemics propagated on contact networks

Detecting protein complexes in PPI networks: The roles of interactions

Strong ties promote the epidemic prevalence in susceptible–infected–susceptible spreading dynamics

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