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A Density-Based Approach to Detect Community Evolutionary Events in Online Social Networks

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State of the Art Applications of Social Network Analysis

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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Abstract

With the advent of Web 2.0/3.0 supported social media, Online Social Networks (OSNs) have emerged as one of the popular communication tools to interact with similar interest groups around the globe. Due to increasing popularity of OSNs and exponential growth in the number of their users, a significant amount of research efforts has been diverted towards analyzing user-generated data available on these networks, and as a result various community mining techniques have been proposed by different research groups. But, most of the existing techniques consider the number of OSN users as a fixed set, which is not always true in a real scenario, rather the OSNs are dynamic in the sense that many users join/leave the network on a regular basis. Considering such dynamism, this chapter presents a density-based community mining method, OCTracker, for tracking overlapping community evolution in online social networks. The proposed approach adapts a preliminary community structure towards dynamic changes in social networks using a novel density-based approach for detecting overlapping community structures and automatically detects evolutionary events including birth, growth, contraction, merge, split, and death of communities with time. Unlike other density-based community detection methods, the proposed method does not require the neighborhood threshold parameter to be set by the users, rather it automatically determines the same for each node locally. Evaluation results on various datasets reveal that the proposed method is computationally efficient and naturally scales to large social networks.

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Notes

  1. 1.

    For a directed network two nodes are said to be reciprocating if each has an out-going edge towards the other, whereas for un-directed networks each edge is considered to represent a bi-directional reciprocal edge.

  2. 2.

    Figure 3 does not depict the actual size of the detected communities or the amount of overlap between communities.

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Authors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, 110025, India

    Muhammad Abulaish & Sajid Yousuf Bhat

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  1. Muhammad Abulaish
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  2. Sajid Yousuf Bhat
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Correspondence to Muhammad Abulaish .

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Editors and Affiliations

  1. Department of Computer Engineering, Bilkent University, Bilkent, Turkey

    Fazli Can

  2. Department of Computer Engineering, TOBB University, Sogutozu Ankara, Turkey

    Tansel Özyer

  3. Department of Computer Engineering University Campus, Middle East Technical University, Ankara, Turkey

    Faruk Polat

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Abulaish, M., Bhat, S.Y. (2014). A Density-Based Approach to Detect Community Evolutionary Events in Online Social Networks. In: Can, F., Özyer, T., Polat, F. (eds) State of the Art Applications of Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-05912-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-05912-9_9

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-05912-9

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