Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks

Ayan Kumar Bhowmick, Martin Gueuning, Jean Charles Delvenne, Renaud Lambiotte, Bivas Mitra

Résultats de recherche: Contribution à un journal/une revueArticle

Résumé

Identification of influential users in online social networks allows to facilitate efficient information diffusion to a large part of the network and thus benefiting diverse applications including viral marketing, disease control, and news dissemination. Existing methods have mainly relied on the network structure only for the detection of influential users. In this paper, we enrich this approach by proposing a fast, efficient, and unsupervised algorithm SmartInf to detect a set of influential users by identifying anchor nodes from a temporal sequence of retweets in Twitter cascades. Such anchor nodes provide important signatures of tweet diffusion across multiple diffusion localities and, hence, act as precursors for detection of influential nodes.1 The set of influential nodes identified by SmartInf has the capacity to expose the tweet to a large and diverse population, when targeted as seeds thereby maximizing the influence spread. Experimental evaluation on empirical datasets from Twitter shows the superiority of SmartInf over state-of-the-art baselines in terms of infecting larger population; further, our evaluation shows that SmartInf is scalable to large-scale networks and is robust to missing data. Finally, we investigate the key factors behind the improved performance of SmartInf by testing our algorithm on a synthetic network using synthetic cascades simulated on this network. Our results reveal the effectiveness of SmartInf in identifying a diverse set of influential users that facilitate faster diffusion of tweets to a larger population.1We use the terms 'influential nodes' and 'influential users' interchangeably.

langue originaleAnglais
Numéro d'article8692729
Pages (de - à)441-455
Nombre de pages15
journalIEEE Transactions on Computational Social Systems
Volume6
Numéro de publication3
Les DOIs
étatPublié - 1 juin 2019

Empreinte digitale

Social Networks
social network
Vertex of a graph
Anchors
twitter
Cascade
Disease control
Fast Diffusion
Information Diffusion
Seed
Marketing
Missing Data
evaluation
Experimental Evaluation
Locality
Network Structure
Precursor
Baseline
news
Signature

Citer ceci

@article{1377348aa18f4b08b634f294337248a5,
title = "Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks",
abstract = "Identification of influential users in online social networks allows to facilitate efficient information diffusion to a large part of the network and thus benefiting diverse applications including viral marketing, disease control, and news dissemination. Existing methods have mainly relied on the network structure only for the detection of influential users. In this paper, we enrich this approach by proposing a fast, efficient, and unsupervised algorithm SmartInf to detect a set of influential users by identifying anchor nodes from a temporal sequence of retweets in Twitter cascades. Such anchor nodes provide important signatures of tweet diffusion across multiple diffusion localities and, hence, act as precursors for detection of influential nodes.1 The set of influential nodes identified by SmartInf has the capacity to expose the tweet to a large and diverse population, when targeted as seeds thereby maximizing the influence spread. Experimental evaluation on empirical datasets from Twitter shows the superiority of SmartInf over state-of-the-art baselines in terms of infecting larger population; further, our evaluation shows that SmartInf is scalable to large-scale networks and is robust to missing data. Finally, we investigate the key factors behind the improved performance of SmartInf by testing our algorithm on a synthetic network using synthetic cascades simulated on this network. Our results reveal the effectiveness of SmartInf in identifying a diverse set of influential users that facilitate faster diffusion of tweets to a larger population.1We use the terms 'influential nodes' and 'influential users' interchangeably.",
keywords = "Anchor nodes, cascades, inter-retweet time intervals, Smartinf",
author = "Bhowmick, {Ayan Kumar} and Martin Gueuning and Delvenne, {Jean Charles} and Renaud Lambiotte and Bivas Mitra",
year = "2019",
month = "6",
day = "1",
doi = "10.1109/TCSS.2019.2907553",
language = "English",
volume = "6",
pages = "441--455",
journal = "IEEE Transactions on Computational Social Systems",
issn = "2329-924X",
publisher = "Institute of Electrical and Electronics Engineers",
number = "3",

}

Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks. / Bhowmick, Ayan Kumar; Gueuning, Martin; Delvenne, Jean Charles; Lambiotte, Renaud; Mitra, Bivas.

Dans: IEEE Transactions on Computational Social Systems, Vol 6, Numéro 3, 8692729, 01.06.2019, p. 441-455.

Résultats de recherche: Contribution à un journal/une revueArticle

TY - JOUR

T1 - Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks

AU - Bhowmick, Ayan Kumar

AU - Gueuning, Martin

AU - Delvenne, Jean Charles

AU - Lambiotte, Renaud

AU - Mitra, Bivas

PY - 2019/6/1

Y1 - 2019/6/1

N2 - Identification of influential users in online social networks allows to facilitate efficient information diffusion to a large part of the network and thus benefiting diverse applications including viral marketing, disease control, and news dissemination. Existing methods have mainly relied on the network structure only for the detection of influential users. In this paper, we enrich this approach by proposing a fast, efficient, and unsupervised algorithm SmartInf to detect a set of influential users by identifying anchor nodes from a temporal sequence of retweets in Twitter cascades. Such anchor nodes provide important signatures of tweet diffusion across multiple diffusion localities and, hence, act as precursors for detection of influential nodes.1 The set of influential nodes identified by SmartInf has the capacity to expose the tweet to a large and diverse population, when targeted as seeds thereby maximizing the influence spread. Experimental evaluation on empirical datasets from Twitter shows the superiority of SmartInf over state-of-the-art baselines in terms of infecting larger population; further, our evaluation shows that SmartInf is scalable to large-scale networks and is robust to missing data. Finally, we investigate the key factors behind the improved performance of SmartInf by testing our algorithm on a synthetic network using synthetic cascades simulated on this network. Our results reveal the effectiveness of SmartInf in identifying a diverse set of influential users that facilitate faster diffusion of tweets to a larger population.1We use the terms 'influential nodes' and 'influential users' interchangeably.

AB - Identification of influential users in online social networks allows to facilitate efficient information diffusion to a large part of the network and thus benefiting diverse applications including viral marketing, disease control, and news dissemination. Existing methods have mainly relied on the network structure only for the detection of influential users. In this paper, we enrich this approach by proposing a fast, efficient, and unsupervised algorithm SmartInf to detect a set of influential users by identifying anchor nodes from a temporal sequence of retweets in Twitter cascades. Such anchor nodes provide important signatures of tweet diffusion across multiple diffusion localities and, hence, act as precursors for detection of influential nodes.1 The set of influential nodes identified by SmartInf has the capacity to expose the tweet to a large and diverse population, when targeted as seeds thereby maximizing the influence spread. Experimental evaluation on empirical datasets from Twitter shows the superiority of SmartInf over state-of-the-art baselines in terms of infecting larger population; further, our evaluation shows that SmartInf is scalable to large-scale networks and is robust to missing data. Finally, we investigate the key factors behind the improved performance of SmartInf by testing our algorithm on a synthetic network using synthetic cascades simulated on this network. Our results reveal the effectiveness of SmartInf in identifying a diverse set of influential users that facilitate faster diffusion of tweets to a larger population.1We use the terms 'influential nodes' and 'influential users' interchangeably.

KW - Anchor nodes

KW - cascades

KW - inter-retweet time intervals

KW - Smartinf

UR - http://www.scopus.com/inward/record.url?scp=85064643408&partnerID=8YFLogxK

U2 - 10.1109/TCSS.2019.2907553

DO - 10.1109/TCSS.2019.2907553

M3 - Article

AN - SCOPUS:85064643408

VL - 6

SP - 441

EP - 455

JO - IEEE Transactions on Computational Social Systems

JF - IEEE Transactions on Computational Social Systems

SN - 2329-924X

IS - 3

M1 - 8692729

ER -