Mining structural hole spanners through information diffusion in social networks

Lou, Tiancheng; Tang, Jie

doi:10.1145/2488388.2488461

Cited by 143 publications

(101 citation statements)

References 31 publications

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“…The concept of bridge nodes in general has been studied in previous works in terms of role discovery [14,27,9] (see details in related work). However, all of above studies assume that bridge nodes structurally connect to homogeneous nodes/communities (such as celebrities).…”

Section: Media Nodesmentioning

confidence: 99%

“…Recent works, like [5] and [8], used techniques like NMF and probabilistic generative model. The most related works to our problem include [9,14,27]. Henderson et al [9] used features to extract different roles of nodes including bridge nodes that connect so called 'main-stream' nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Henderson et al [9] used features to extract different roles of nodes including bridge nodes that connect so called 'main-stream' nodes. Lou et al [14] and Yang et al [27] detected structural hole spanners which bridge homogeneous communities. However, our media nodes are qualitatively different, and all existing works do not take diffusive properties of the bridge nodes into account the way we do (see Section 2).…”

Section: Related Workmentioning

confidence: 99%

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MeiKe: Influence-based Communities in Networks

Zhang

Adhikari

Jan

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Given a social network, how to find communities of nodes based on their diffusive characteristics? There exist two important types of nodes, for information propagation: nodes that are influential ("kernel nodes"), and nodes that serve as "bridges" to boost the diffusion ("media nodes"). How to find these nodes and uncover connections between them? In addition, it is also important to discover the hidden community structure of these nodes, which can help study their interactions, predict links and also understand the information flow in such networks.In this paper, we give an intuitive and novel optimization-based formulation for this task, which aims to discover media nodes as well as community structures of kernel nodes. We prove our task is computationally challenging, and develop an effective and practical algorithm MeiKe (pronounced as 'Mike'). It first obtains media nodes via a new successive summarization based approach, and then finds kernel nodes including their community structures. Experimental results show that MeiKe finds high-quality media and kernel communities which match our expectations and ground-truth (outperforming non-trivial baselines by 40% in F1-score). Our case studies also demonstrate the applicability of MeiKe on a variety of datasets.

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Section: Media Nodesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

MeiKe: Influence-based Communities in Networks

Zhang

Adhikari

Jan

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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“…This model is also applicable to viral marketing diffusion because it is applied on the same constrained environment and has the same objective Some other methods, such as influencer selection, use clustering for identification problems. Lou and Tang [48] developed a model for mining top-k structural hole spanners in large-scale social networks. The general idea behind this was to measure how a node bridges different communities.…”

Section: ) Clusteringmentioning

confidence: 99%

Toward Information Diffusion Model for Viral Marketing in Business

AlSuwaidan¹,

Ykhlef²

2016

ijacsa

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Abstract-Current obstacles in the study of social media marketing include dealing with massive data and real-time updates have motivated to contribute solutions that can be adopted for viral marketing. Since information diffusion and social networks are the core of viral marketing, this article aims to investigate the constellation of diffusion methods for viral marketing. Studies on diffusion methods for viral marketing have applied different computational methods, but a systematic investigation of these methods has limited. Most of the literature have focused on achieving objectives such as influence maximization or community detection. Therefore, this article aims to conduct an in-depth review of works related to diffusion for viral marketing. Viral marketing has applied to business-to-consumer transactions but has seen limited adoption in business-to-business transactions. The literature review reveals a lack of new diffusion methods, especially in dynamic and large-scale networks. It also offers insights into applying various mining methods for viral marketing. It discusses some of the challenges, limitations, and future research directions of information diffusion for viral marketing. The article also introduces a viral marketing information diffusion model. The proposed model attempts to solve the dynamicity and large-scale data of social networks by adopting incremental clustering and a stochastic differential equation for business-to-business transactions.

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“…We choose some node characteristics including Out-Degree, OutDegree, Average Degree, PageRank[11] and Structural Hole [12] as indicators to observe the influential users in information diffusion.…”

Section: Experiments and Evalutionmentioning

confidence: 99%