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International Conference on Research in Networking

NETWORKING 2012: NETWORKING 2012 pp 56–67Cite as

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Crawling and Detecting Community Structure in Online Social Networks Using Local Information

Crawling and Detecting Community Structure in Online Social Networks Using Local Information

  • Norbert Blenn20,
  • Christian Doerr20,
  • Bas Van Kester20 &
  • …
  • Piet Van Mieghem20 
  • Conference paper
  • 1826 Accesses

  • 7 Citations

Part of the Lecture Notes in Computer Science book series (LNCCN,volume 7289)

Abstract

As Online Social Networks (OSNs) become an intensive subject of research for example in computer science, networking, social sciences etc., a growing need for valid and useful datasets is present. The time taken to crawl the network is however introducing a bias which should be minimized. Usual ways of addressing this problem are sampling based on the nodes (users) ids in the network or crawling the network until one “feels” a sufficient amount of data has been obtained.

In this paper we introduce a new way of directing the crawling procedure to selectively obtain communities of the network. Thus, a researcher is able to obtain those users belonging to the same community and rapidly begin with the evaluation. As all users involved in the same community are crawled first, the bias introduced by the time taken to crawl the network and the evolution of the network itself is less.

Our presented technique is also detecting communities during runtime. We compare our method called Mutual Friend Crawling (MFC) to the standard methods Breadth First Search (BFS) and Depth First Search (DFS) and different community detection algorithms. The presented results are very promising as our method takes only linear runtime but is detecting equal structures as modularity based community detection algorithms.

Keywords

  • Social Networks
  • Community Detection
  • Crawling

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References

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Author information

Authors and Affiliations

  1. Department of Telecommunication, TU Delft, Mekelweg 4, 2628CD, Delft, The Netherlands

    Norbert Blenn, Christian Doerr, Bas Van Kester & Piet Van Mieghem

Authors
  1. Norbert Blenn
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  2. Christian Doerr
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  3. Bas Van Kester
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  4. Piet Van Mieghem
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Editor information

Editors and Affiliations

  1. Department of Telecommunications Engineering, Czech Technical University in Prague, Technicka 2, 166 27, Prague 6, Czech Republic

    Robert Bestak & Lukas Kencl & 

  2. Alcatel-Lucent, Bell Labs, 600 Mountain Avenue, 07974-0636, Murray Hill, NJ, USA

    Li Erran Li

  3. Instituto IMDEA Networks, Avenida del Mar Mediterraneo 22, Leganes, 28918, Madrid), Spain

    Joerg Widmer

  4. Tsinghua-ChinaCache Joint Laboratory, Tsinghua University, FIT 3-429, Haidian District, 100016, Beijing, China

    Hao Yin

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© 2012 IFIP International Federation for Information Processing

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Cite this paper

Blenn, N., Doerr, C., Van Kester, B., Van Mieghem, P. (2012). Crawling and Detecting Community Structure in Online Social Networks Using Local Information. In: Bestak, R., Kencl, L., Li, L.E., Widmer, J., Yin, H. (eds) NETWORKING 2012. NETWORKING 2012. Lecture Notes in Computer Science, vol 7289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30045-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-30045-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30044-8

  • Online ISBN: 978-3-642-30045-5

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