Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2660460.2660480acmconferencesArticle/Chapter ViewAbstractPublication PagescosnConference Proceedingsconference-collections
research-article

Inferring coarse views of connectivity in very large graphs

Published: 01 October 2014 Publication History
  • Get Citation Alerts
  • Abstract

    This paper presents a simple framework, called WalkAbout, to infer a coarse view of connectivity in very large graphs; that is, identify well-connected "regions" with different edge densities and determine the corresponding inter- and intra-region connectivity. We leverage the transient behavior of many short random walks (RW) on a large graph that is assumed to have regions of varying edge density but whose structure is otherwise unknown. The key idea is that as RWs approach the mixing time of a region, the ratio of the number of visits by all RWs to the degree for nodes in that region converges to a value proportional to the average node degree in that region. Leveraging this indirect sign of connectivity enables our proposed framework to effectively scale with graph size.
    After describing the design of WalkAbout, we demonstrate the capabilities of WalkAbout by applying it to three major OSNs ie Flickr, Twitter, and Google+) and obtaining a coarse view of their connectivity structure. In addition, we illustrate how the communities that are obtained by running a popular community detection method on these OSNs stack up against the WalkAbout-discovered regions. Finally, we examine the "meaning" of the regions obtained by WalkAbout, and demonstrate that users in the identified regions exhibit common social attributes.

    Supplementary Material

    TXT File (cosn080f.txt)
    Details
    ZIP File (cosn080f.zip)
    See Read Me file "cosn080f.txt"

    References

    [1]
    Y. Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. Analysis of Topological Characteristics of Huge Online Social Networking Services. In Proc. of ACM WWW, pages 835--844, 2007.
    [2]
    R. Andersen, F. Chung, and K. Lang. Local Graph Partitioning using Pagerank Vectors. In Proc. of IEEE FoCS, pages 475--486, 2006.
    [3]
    M. Bastian, S. Heymann, M. Jacomy, et al. Gephi : An Open Source Software for Exploring and Manipulating Networks. ICWSM, 8:361--362,2009.
    [4]
    V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008,2008.
    [5]
    F. Chung. Spectral Graph Theory. CBMS Regional Conference Series. Conference Board of the Mathematical Sciences, 1997.
    [6]
    I. S. Dhillon, Y. Guan, and B. Kulis. Kernel k-means: Spectral Clustering and Normalized Cuts. In Proc. of ACM SIGKDD, pages551--556,2004.
    [7]
    I. S. Dhillon, Y. Guan, and B. Kulis. Weighted Graph Cuts without Eigenvectors: A Multilevel Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(11):1944--1957,2007.
    [8]
    H. N. Djidjev. A Scalable Multilevel Algorithm for Graph Clustering and Community Structure Detection. In Algorithms and Models for the Web-Graph, pages 117--128. Springer, 2008.
    [9]
    S. Fortunato. Community detection in graphs. Physics Reports, 486(3):75--174,2010.
    [10]
    R. Gonzalez, R. Cuevas, R. Motamedi, R. Rejaie, and A. Cuevas. Google+ or Google-?: Dissecting the Evolution of the New OSN in its First Year. In Proc. of ACM WWW, 2013.
    [11]
    R. Kannan, S. Vempala, and A. Vetta. On Clusterings: Good, Bad and Spectral. Journal of the ACM, 51(3):497--515,2004.
    [12]
    G. Karypis and V. Kumar. METIS - Unstructured Graph Partitioning and Sparse Matrix Ordering System, Version 2.0, 1995.
    [13]
    G. Karypis and V. Kumar. A FAST AND HIGH QUALITY MULTILEVEL SCHEME FOR PARTITIONING IRREGULAR GRAPHS. SIAM Journal on scientific Computing, 20(1):359--392,1998.
    [14]
    J. M. Kleinberg, R. Kumar, P. Raghavan, S. Rajagopalan, and A. S. Tomkins. The Web as a graph: measurements, models, and methods. In Computing and combinatorics, pages 1--17. Springer, 1999.
    [15]
    H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a Social Network or a News Media? In Proc. of ACM WWW, pages 591--600, 2010.
    [16]
    J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney. Community Structure in Large Networks: Natural Cluster Sizes and The Absence of Large Well-Defined Clusters. Internet Mathematics, 6(1):29--123, 2009.
    [17]
    L. Lovász. Random Walks on Graphs: A Survey. Combinatorics, Paul erdos is eighty, 2(1):1--46,1993.
    [18]
    A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and Analysis of Online Social Networks. In Proc. of ACM IMC, pages 29--42, 2007.
    [19]
    R. Motamedi, R. Rejaie, D. Lowd, and W. Willinger. WalkAbout: Exploring the Regional Connectivity of Large Graphs and Its Application to OSNs. Technical report available at: http://onrg.cs.uoregon.edu/pub/tr13-06.pdf, University of Oregon, 2014.
    [20]
    M. Nerenberg, R. Motamedi, and R. Rejaie. Interactive Graph Coarsening by WalkAbout. Code available at: http://onrg.cs.uoregon.edu/WalkAbout, University of Oregon, 2014.
    [21]
    M. E. Newman. Modularity and community structure in networks. Proc. of the National Academy of Sciences, 103(23):8577--8582,2006.
    [22]
    P. Pons and M. Latapy. Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications, 10(2),2006.
    [23]
    V. Satuluri and S. Parthasarathy. Scalable Graph Clustering Using Stochastic Flows: Applications to Community Discovery. In Proc. of ACM SIGKDD, pages 737--746, 2009.
    [24]
    D. Stutzbach, R. Rejaie, and S. Sen. Characterizing Unstructured Overlay Topologies in Modern P2P File-Sharing Systems. IEEE/ACM Transactions on Networking, 16(2):267--280,2008.
    [25]
    S. Van Dongen. A cluster algorithm for graphs. Report-Information systems, (10):1--40,2000.
    [26]
    S. White and P. Smyth. A Spectral Clustering Approach To Finding Communities in Graph. In Proc. of SIAM SDM, volume 5, pages 76--84, 2005.

    Cited By

    View all
    • (2019)Examining the evolution of the Twitter elite networkSocial Network Analysis and Mining10.1007/s13278-019-0612-810:1Online publication date: 27-Nov-2019
    • (2018)On characterizing the Twitter elite networkProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3382225.3382276(234-241)Online publication date: 28-Aug-2018
    • (2018)On Characterizing the Twitter Elite Network2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2018.8508252(234-241)Online publication date: Aug-2018

    Index Terms

    1. Inferring coarse views of connectivity in very large graphs

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      COSN '14: Proceedings of the second ACM conference on Online social networks
      October 2014
      288 pages
      ISBN:9781450331982
      DOI:10.1145/2660460
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 01 October 2014

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. clustering
      2. community detection
      3. graph coarsening
      4. graph partitioning
      5. scalability

      Qualifiers

      • Research-article

      Funding Sources

      Conference

      COSN'14
      Sponsor:
      COSN'14: Conference on Online Social Networks
      October 1 - 2, 2014
      Dublin, Ireland

      Acceptance Rates

      COSN '14 Paper Acceptance Rate 25 of 87 submissions, 29%;
      Overall Acceptance Rate 69 of 307 submissions, 22%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 12 Aug 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2019)Examining the evolution of the Twitter elite networkSocial Network Analysis and Mining10.1007/s13278-019-0612-810:1Online publication date: 27-Nov-2019
      • (2018)On characterizing the Twitter elite networkProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3382225.3382276(234-241)Online publication date: 28-Aug-2018
      • (2018)On Characterizing the Twitter Elite Network2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2018.8508252(234-241)Online publication date: Aug-2018

      View Options

      Get Access

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media