A New Centrality Measure for Influence Maximization in Social Networks

Kundu, Suman; Murthy, C. A.; Pal, Sankar K.

doi:10.1007/978-3-642-21786-9_40

Cited by 60 publications

(20 citation statements)

References 9 publications

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“…For this research study, we were only considered undirected, loop-free connected graphs according to the PPIN topology. For centrality analysis, the following 27 centrality measures were selected: Average Distance [ 43 ], Barycenter [ 44 ], Closeness (Freeman) [ 9 ], Closeness (Latora) [ 45 ], Residual closeness [ 46 ], ClusterRank [ 47 ], Decay [ 48 ], Diffusion degree [ 49 ], Density of Maximum Neighborhood Component (DMNC) [ 50 ], Geodesic K-Path [ 51 , 52 ], Katz [ 53 , 54 ], Laplacian [ 55 ], Leverage [ 56 ], Lin [ 57 ], Lobby [ 58 ], Markov [ 59 ], Maximum Neighborhood Component (MNC) [ 50 ], Radiality [ 60 ], Eigenvector [ 61 ], Subgraph scores [ 62 ], Shortest-Paths betweenness [ 9 ], Eccentricity [ 63 ], Degree, Kleinberg’s authority scores [ 64 ], Kleinberg’s hub scores [ 64 ], Harary graph [ 63 ] and Information [ 65 ]. All these measures are calculated for undirected networks in a reasonable time.…”

Section: Methodsmentioning

confidence: 99%

A systematic survey of centrality measures for protein-protein interaction networks

et al. 2018

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BackgroundNumerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.ResultsWe used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network’s topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.ConclusionsThe choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0598-2) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

A systematic survey of centrality measures for protein-protein interaction networks

et al. 2018

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“…In [25], two basic diffusion models are investigated: Linear Thresholds Model and Independent cascade Model. Kundu et al [26] propose an independent cascade model and a centrality measure in order to find top k influential nodes in large scale directed social networks. In [2] authors propose a frequent pattern mining approach to discover leaders in social networks, studying the propagation of their "influence", while in [11] authors develop an approach to determine "influencing users" based on a nonstandard form of Bayesian shrinkage.…”

Section: Related Workmentioning

confidence: 99%

Identifying Opinion Leaders in Time-Dependent Commercial Social Networks

Volpentesta

Felicetti

2012

IFIP Advances in Information and Communication Technology

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Abstract. The increasing amount of information flowing through commercial social networks offers clear advantages for companies who can take a valuable feedback from community actions. In particular, the identification of influential users in on-line social network can support companies in designing and targeting marketing campaigns, as influential gate-keepers and diffusers of information can ignite epidemics through word-of-mouth. In this paper, we model a time-dependent commercial social network as a time-varying weighted directed graph. Moreover, we propose an approach to determine opinion leaders and their contributions to a temporal business value, by taking into account behavioural and structural aspects of the commercial social network.

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“…Besides these measures based on the eigenvector centrality, there exist other centrality measures based on influence spread on the independent cascade model [26,27,28]. Under this model, each actor has a probability to influence the actors he targets.…”

Section: Centrality Under Influence Criteriamentioning

confidence: 99%

“…Indeed, the influence maximization problem under the linear threshold model is NP-hard [13]. The studies about derived centrality measures are scarce and consider only the independent cascade model [26,27,28]. Those rankings were proposed, and evaluated, to get good solutions to the influence maximization problem.…”

Section: Introductionmentioning

confidence: 99%

Centrality measure in social networks based on linear threshold model

Riquelme

González-Cantergiani²,

Molinero

et al. 2018

Knowledge-Based Systems

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Centrality and influence spread are two of the most studied concepts in social network analysis. In recent years, centrality measures have attracted the attention of many researchers, generating a large and varied number of new studies about social network analysis and its applications. However, as far as we know, traditional models of influence spread have not yet been exhaustively used to define centrality measures according to the influence criteria. Most of the considered work in this topic is based on the independent cascade model. In this paper we explore the possibilities of the linear threshold model for the definition of centrality measures to be used on weighted and labeled social networks. We propose a new centrality measure to rank the users of the network, the Linear Threshold Rank (LTR), and a centralization measure to determine to what extent the entire network has a centralized structure, the Linear Threshold Centralization (LTC). We appraise the viability of the approach through several case studies. We consider four different social networks to compare our new measures with two centrality measures based on relevance criteria and another centrality measure based on the independent cascade model. Our results show that our measures are useful for ranking actors and networks in a distinguishable way.Peer ReviewedPostprint (author's final draft

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A New Centrality Measure for Influence Maximization in Social Networks

Cited by 60 publications

References 9 publications

A systematic survey of centrality measures for protein-protein interaction networks

A systematic survey of centrality measures for protein-protein interaction networks

Identifying Opinion Leaders in Time-Dependent Commercial Social Networks

Centrality measure in social networks based on linear threshold model

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