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Influence Diffusion, Community Detection, and Link Prediction in Social Network Analysis

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Book cover Dynamics of Information Systems: Algorithmic Approaches

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 51))

Abstract

Social networks have received extensive attention among researchers across a wide range of disciplines such as computer science, physics, and sociology. This paper mainly overviews a variety of approaches for three problems in real-world life scenarios. The first problem is about influence diffusion, in which influence represents news, ideas, information, and so forth; the second one concerns with partitioning social networks into communities efficiently; and the third one is to predict the hidden or possible new links between individuals in the future based on the existing or observed information.

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Authors and Affiliations

  1. Department of Computer Science, University of Texas at Dallas, Richardson, TX, 75080, USA

    Lidan Fan, Weili Wu, Zaixin Lu, Wen Xu & Ding-Zhu Du

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  1. Lidan Fan
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  2. Weili Wu
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  3. Zaixin Lu
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  4. Wen Xu
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  5. Ding-Zhu Du
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Correspondence to Ding-Zhu Du .

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Editors and Affiliations

  1. Innovative Scheduling, Inc., Gainesville, Florida, USA

    Alexey Sorokin

  2. Department of Industrial and Systems Eng, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

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Fan, L., Wu, W., Lu, Z., Xu, W., Du, DZ. (2013). Influence Diffusion, Community Detection, and Link Prediction in Social Network Analysis. In: Sorokin, A., Pardalos, P. (eds) Dynamics of Information Systems: Algorithmic Approaches. Springer Proceedings in Mathematics & Statistics, vol 51. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7582-8_11

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