Identification of influential spreaders in complex networks using HybridRank algorithm

Ahajjam, Sara; Badir, Hassan

doi:10.1038/s41598-018-30310-2

Cited by 87 publications

(46 citation statements)

References 38 publications

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“…Centrality which is de ned as the number of connections with the other nodes in the network were widely proved to be associated with the dynamics of transmission of infectious diseases [30,31]. However, centrality measures are simple but maybe less effective for they neglect the whole structure of the network [32,33]. From our analysis above, we can see that it is easy to ignore some important nodes and subgroups by calculating centrality to identify key nodes.…”

Section: Discussionmentioning

confidence: 98%

Identifying the key nodes of HIV molecular transmission network among men who have sex with men in Guangzhou, China: A cross-sectional study

Yang

Han

et al. 2020

Preprint

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Background Identifying the most influential spreaders in HIV transmission networks is crucial to develop effective prevention strategies. The purpose of this study is to identify the key nodes of the molecular transmission network among MSM, which has significant insight into developing effective HIV prevention strategies. Methods We analyzed HIV-1 pol sequences provided through the Center for Disease Control and Prevention, Guangzhou, China. Sequences were obtained from newly-HIV diagnosed MSM during 2015–2017. We calculated pairwise genetic distance, identified linked pairs of sequences (those with distance ≤ 1.5%), and examined key nodes of these potential transmission partners. Results Of 184 HIV recently diagnosed men who have sex with men (MSM), 40.76% were linked to other MSM. Social network analysis demonstrated that there were 9 key nodes were detected. From the results of multivariate logistic regression model, young MSM born in the 1990s and 1980s was 0.06 and 0.12 times as likely to be a key node as was the older MSM born in the 1970s and before. Conclusions There were a lot of subgroups connected by sharing comembers in HIV molecular transmission network among MSM in Guangzhou. Some HIV-infected MSM called as key nodes mediated the transmission of HIV among different subpopulations. Young MSM were less likely to promote HIV transmission than older MSM.

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Section: Discussionmentioning

confidence: 98%

Identifying the key nodes of HIV molecular transmission network among men who have sex with men in Guangzhou, China: A cross-sectional study

Yang

Han

et al. 2020

Preprint

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“…Betweenness centrality is based on the shortest paths, wherein a stakeholder can, indeed, reach other stakeholders (neighbouring nodes) through different paths, but only the shortest one is computed [37]. Eigenvector centrality is a proxy of the influence of a stakeholder and its importance in terms of connections with high-scoring (‘central’) nodes [38]. To carry out SNA, average scores were used based on the views of the participants, as well as the association of each organisation/institution with all the other organisations.…”

Section: Methodsmentioning

confidence: 99%

Hepatitis C virus-related policy-making in Iran: a stakeholder and social network analysis

Behzadifar

Gorji

Rezapour

et al. 2019

Health Res Policy Sys

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Background Hepatitis C virus (HCV) infection is a major public health challenge worldwide. Implementing policies to cope with this challenge requires commitment from all stakeholders at various levels, and all necessary resources should be mobilised. Support for various HCV-related stakeholders can reduce the challenges and obstacles that can be encountered during the programme implementation. The present study aims to identify all stakeholders involved with HCV-related policy-making in Iran at different steps (policy development, implementation and evaluation) and to characterise them in terms of interest, position, power and influence, in order to provide valuable information for appropriate decision-making and design. The present study can also serve as a case study for healthcare systems in other countries. Method An approach based on social network analysis was utilised. Data collected included relevant document searches and in-depth interviews to a sample of 18 key informants. Results Various stakeholders were found to be involved with HCV-related policies in Iran. The extent of their participation and support in policy-making varied. Specifically, international agencies had a high interest for HCV-related policy-making, whereas media and members of the private sector were characterised by a medium interest and governmental and non-governmental bodies by a highly variable interest, ranging from low to high, depending on the specific organism. Moreover, media and members of the private sector, non-governmental institutions and international agencies were rated low in terms of position, whereas governmental actors were rated low to high. Media were rated medium in terms of power, whereas international agencies and members of the private sector were respectively rated low to medium and low. Non-governmental actors were rated low, whilst governmental bodies were rated low to high. Finally, media, members of the private sector and international agencies were rated medium in terms of influence, whereas non-governmental and governmental actors were respectively rated low to medium and low to high. Conclusion Policy-making involves trust, negotiation and integration of the different views of all stakeholders. Social network analysis was critical for identifying stakeholders and showing that, in Iran, involvement in HCV-related policy-making is generally low. This information is of practical implication for policy- and decision-makers regarding the adoption of more favourable and effective strategies.

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“…This can be useful for identifying the most important network nodes, with very different interpretations. On one side of the coin, in science co-authorship networks, determining which node removals produce higher information spreading reduction may help us to understand who are the nodes/scientists making the greatest contribution to knowledge and idea spreading [30,56,57]. These findings may furnish useful tools for designing policies facilitating the activities of these scholars, who act as "influential spreaders" in the network.…”

Section: Node Removal In Social Networkmentioning

confidence: 99%

Link and Node Removal in Real Social Networks: A Review

et al. 2020

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We review the main results from the literature on the consequences of link and node removal in real social networks. We restrict our review to only those works that adopted the two most common measures of network robustness, i.e., the largest connected component (LCC) and network efficiency (Eff). We consider both binary and weighted network approaches. We show that the study of the response of social networks subjected to link/node removal turns out to be extremely useful for managing a number of real problems. For instance, we show that the consequences of the imposition of social distancing in many states to control the spread of COVID-19 could be analyzed within the framework of social network analysis. Our mini-review outlines that in social networks, it is necessary to consider the weight of links between persons to perform reliable analyses. Finally, we propose promising lines for future research in social network science.

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Identification of influential spreaders in complex networks using HybridRank algorithm

Cited by 87 publications

References 38 publications

Identifying the key nodes of HIV molecular transmission network among men who have sex with men in Guangzhou, China: A cross-sectional study

Identifying the key nodes of HIV molecular transmission network among men who have sex with men in Guangzhou, China: A cross-sectional study

Hepatitis C virus-related policy-making in Iran: a stakeholder and social network analysis

Link and Node Removal in Real Social Networks: A Review

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