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Mining Community Structure with Node Embeddings

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From Social Data Mining and Analysis to Prediction and Community Detection

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

Abstract

We develop node embeddings, a distributed representation of nodes, for large-scale social network applications. We compute embeddings for nodes based on their attributes and links. We show that node embeddings can effectively reflect community structure in networks and thus, be useful for a wide range of community related applications. We consider node embeddings in two different community related mining tasks.First, we propose a generic integration of node embeddings for network processing in community detection algorithms. Our strategy aims to re-adjust input networks by adding and trimming links, using embedding-based node distances. We empirically show that the strategy can remove up to 32.16% links from the DBLP (computer science literature) citation network, yet improve performance for different algorithms by different evaluation metrics for community detections.Second, we show that these embeddings can support many community-based mining tasks in social networks—including analyses of community homogeneity, distance, and detection of community connectors (inter-community outliers, actors who connect communities)—thanks to the convenient yet efficient computation provided by node embeddings for structural comparisons. Our experimental results include many interesting insights about DBLP. For example, prior to 2013 the best way for research in Natural Language & Speech to gain “best-paper” recognition was to emphasize aspects related to Machine Learning & Pattern Recognition.

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Authors and Affiliations

  1. Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA

    Thuy Vu & D. Stott Parker

Authors
  1. Thuy Vu
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  2. D. Stott Parker
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Correspondence to Thuy Vu .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Firat University, Elazig, Turkey

    Mehmet Kaya

  2. Ministry of Interior, Ankara, Turkey

    Özcan Erdoǧan

  3. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

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Vu, T., Parker, D.S. (2017). Mining Community Structure with Node Embeddings. In: Kaya, M., Erdoǧan, Ö., Rokne, J. (eds) From Social Data Mining and Analysis to Prediction and Community Detection. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-51367-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-51367-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51366-9

  • Online ISBN: 978-3-319-51367-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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