Cargando…

A graph exploration method for identifying influential spreaders in complex networks

The problem of identifying the influential spreaders - the important nodes - in a real world network is of high importance due to its theoretical interest as well as its practical applications, such as the acceleration of information diffusion, the control of the spread of a disease and the improvem...

Descripción completa

Detalles Bibliográficos
Autores principales: Salamanos, Nikos, Voudigari, Elli, Yannakoudakis, Emmanuel J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214278/
https://www.ncbi.nlm.nih.gov/pubmed/30443581
http://dx.doi.org/10.1007/s41109-017-0047-y
_version_ 1783367955577307136
author Salamanos, Nikos
Voudigari, Elli
Yannakoudakis, Emmanuel J.
author_facet Salamanos, Nikos
Voudigari, Elli
Yannakoudakis, Emmanuel J.
author_sort Salamanos, Nikos
collection PubMed
description The problem of identifying the influential spreaders - the important nodes - in a real world network is of high importance due to its theoretical interest as well as its practical applications, such as the acceleration of information diffusion, the control of the spread of a disease and the improvement of the resilience of networks to external attacks. In this paper, we propose a graph exploration sampling method that accurately identifies the influential spreaders in a complex network, without any prior knowledge of the original graph, apart from the collected samples/subgraphs. The method explores the graph, following a deterministic selection rule and outputs a graph sample - the set of edges that have been crossed. The proposed method is based on a version of Rank Degree graph sampling algorithm. We conduct extensive experiments in eight real world networks by simulating the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) epidemic models which serve as ground truth identifiers of nodes spreading efficiency. Experimentally, we show that by exploring only the 20% of the network and using the degree centrality as well as the k-core measure, we are able to identify the influential spreaders with at least the same accuracy as in the full information case, namely, the case where we have access to the original graph and in that graph, we compute the centrality measures. Finally and more importantly, we present strong evidence that the degree centrality - the degree of nodes in the collected samples - is almost as accurate as the k-core values obtained from the original graph.
format Online
Article
Text
id pubmed-6214278
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-62142782018-11-13 A graph exploration method for identifying influential spreaders in complex networks Salamanos, Nikos Voudigari, Elli Yannakoudakis, Emmanuel J. Appl Netw Sci Research The problem of identifying the influential spreaders - the important nodes - in a real world network is of high importance due to its theoretical interest as well as its practical applications, such as the acceleration of information diffusion, the control of the spread of a disease and the improvement of the resilience of networks to external attacks. In this paper, we propose a graph exploration sampling method that accurately identifies the influential spreaders in a complex network, without any prior knowledge of the original graph, apart from the collected samples/subgraphs. The method explores the graph, following a deterministic selection rule and outputs a graph sample - the set of edges that have been crossed. The proposed method is based on a version of Rank Degree graph sampling algorithm. We conduct extensive experiments in eight real world networks by simulating the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) epidemic models which serve as ground truth identifiers of nodes spreading efficiency. Experimentally, we show that by exploring only the 20% of the network and using the degree centrality as well as the k-core measure, we are able to identify the influential spreaders with at least the same accuracy as in the full information case, namely, the case where we have access to the original graph and in that graph, we compute the centrality measures. Finally and more importantly, we present strong evidence that the degree centrality - the degree of nodes in the collected samples - is almost as accurate as the k-core values obtained from the original graph. Springer International Publishing 2017-08-14 2017 /pmc/articles/PMC6214278/ /pubmed/30443581 http://dx.doi.org/10.1007/s41109-017-0047-y Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Salamanos, Nikos
Voudigari, Elli
Yannakoudakis, Emmanuel J.
A graph exploration method for identifying influential spreaders in complex networks
title A graph exploration method for identifying influential spreaders in complex networks
title_full A graph exploration method for identifying influential spreaders in complex networks
title_fullStr A graph exploration method for identifying influential spreaders in complex networks
title_full_unstemmed A graph exploration method for identifying influential spreaders in complex networks
title_short A graph exploration method for identifying influential spreaders in complex networks
title_sort graph exploration method for identifying influential spreaders in complex networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214278/
https://www.ncbi.nlm.nih.gov/pubmed/30443581
http://dx.doi.org/10.1007/s41109-017-0047-y
work_keys_str_mv AT salamanosnikos agraphexplorationmethodforidentifyinginfluentialspreadersincomplexnetworks
AT voudigarielli agraphexplorationmethodforidentifyinginfluentialspreadersincomplexnetworks
AT yannakoudakisemmanuelj agraphexplorationmethodforidentifyinginfluentialspreadersincomplexnetworks
AT salamanosnikos graphexplorationmethodforidentifyinginfluentialspreadersincomplexnetworks
AT voudigarielli graphexplorationmethodforidentifyinginfluentialspreadersincomplexnetworks
AT yannakoudakisemmanuelj graphexplorationmethodforidentifyinginfluentialspreadersincomplexnetworks