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Social network sampling using spanning trees

Author

Listed:
  • Zeinab S. Jalali

    (Soft Computing Laboratory, Computer Engineering and Information Technology Department, Amirkabir University of Technology (Tehran Polytechnic), Hafez Ave., 424, Tehran, Iran)

  • Alireza Rezvanian

    (Soft Computing Laboratory, Computer Engineering and Information Technology Department, Amirkabir University of Technology (Tehran Polytechnic), Hafez Ave., 424, Tehran, Iran)

  • Mohammad Reza Meybodi

    (Soft Computing Laboratory, Computer Engineering and Information Technology Department, Amirkabir University of Technology (Tehran Polytechnic), Hafez Ave., 424, Tehran, Iran)

Abstract

Due to the large scales and limitations in accessing most online social networks, it is hard or infeasible to directly access them in a reasonable amount of time for studying and analysis. Hence, network sampling has emerged as a suitable technique to study and analyze real networks. The main goal of sampling online social networks is constructing a small scale sampled network which preserves the most important properties of the original network. In this paper, we propose two sampling algorithms for sampling online social networks using spanning trees. The first proposed sampling algorithm finds several spanning trees from randomly chosen starting nodes; then the edges in these spanning trees are ranked according to the number of times that each edge has appeared in the set of found spanning trees in the given network. The sampled network is then constructed as a sub-graph of the original network which contains a fraction of nodes that are incident on highly ranked edges. In order to avoid traversing the entire network, the second sampling algorithm is proposed using partial spanning trees. The second sampling algorithm is similar to the first algorithm except that it uses partial spanning trees. Several experiments are conducted to examine the performance of the proposed sampling algorithms on well-known real networks. The obtained results in comparison with other popular sampling methods demonstrate the efficiency of the proposed sampling algorithms in terms of Kolmogorov–Smirnov distance (KSD), skew divergence distance (SDD) and normalized distance (ND).

Suggested Citation

  • Zeinab S. Jalali & Alireza Rezvanian & Mohammad Reza Meybodi, 2016. "Social network sampling using spanning trees," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(05), pages 1-23, May.
    • Handle: RePEc:wsi:ijmpcx:v:27:y:2016:i:05:n:s0129183116500522
    DOI: 10.1142/S0129183116500522
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    References listed on IDEAS

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    1. Douglas R. White & M. E. J. Newman, 2001. "Fast Approximation Algorithms for Finding Node-Independent Paths in Networks," Working Papers 01-07-035, Santa Fe Institute.
    2. Robert J. Hill, 1999. "International Comparisons Using Spanning Trees," NBER Chapters, in: International and Interarea Comparisons of Income, Output, and Prices, pages 109-120, National Bureau of Economic Research, Inc.
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