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Classification over bipartite graphs through projection


  • STANKOVA, Marija
  • MARTENS, David
  • PROVOST, Foster


Many real-world large datasets correspond to bipartite graph data settings; think for example of users rating movies or people visiting locations. Although some work exists over such bigraphs, no general network-oriented methodology has been proposed yet to perform node classification. In this paper we propose a three-stage classification framework that effectively deals with the typical very large size of such datasets. First, a weighting of the top nodes is defined. Secondly, the bigraph is projected into a unipartite (homogenous) graph among the bottom nodes, where the weights of the edges are a function of the weights of the top nodes in the bigraph. Finally, relational learners/classifiers are applied to the resulting weighted unigraph. This general framework allows us to explore the design space, by applying different choices for the three stages, introducing new alternatives and mixing-and-matching to create new techniques. We present an empirical study of the predictive and run-time performances for different combinations of functions in the three stages over a large collection of bipartite datasets. There are clear differences in predictive performance with different design choices. Based on these results, we propose several specific combinations that show good accuracy and also allow for easy and fast scaling to big datasets. A comparison with a linear SVM method on the adjacency matrix of the bigraph shows the superiority of the network-oriented approach.

Suggested Citation

  • STANKOVA, Marija & MARTENS, David & PROVOST, Foster, 2015. "Classification over bipartite graphs through projection," Working Papers 2015001, University of Antwerp, Faculty of Business and Economics.
  • Handle: RePEc:ant:wpaper:2015001

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    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Seierstad, Cathrine & Opsahl, Tore, 2011. "For the few not the many? The effects of affirmative action on presence, prominence, and social capital of women directors in Norway," Scandinavian Journal of Management, Elsevier, vol. 27(1), pages 44-54, March.
    3. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    4. Ramon Ferrer i Cancho & Ricard V. Solé, 2001. "The Small-World of Human Language," Working Papers 01-03-016, Santa Fe Institute.
    5. Guillaume, Jean-Loup & Latapy, Matthieu, 2006. "Bipartite graphs as models of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 795-813.
    6. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331.
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    Cited by:

    1. DE CNUDDE, Sofie & MOEYERSOMS, Julie & STANKOVA, Marija & TOBBACK, Ellen & JAVALY, Vinayak & MARTENS, David, 2015. "Who cares about your Facebook friends? Credit scoring for microfinance," Working Papers 2015018, University of Antwerp, Faculty of Business and Economics.
    2. TOBBACK, Ellen & MOEYERSOMS, Julie & STANKOVA, Marija & MARTENS, David, 2016. "Bankruptcy prediction for SMEs using relational data," Working Papers 2016004, University of Antwerp, Faculty of Business and Economics.
    3. TOBBACK, Ellen & MARTENS, David, 2017. "Retail credit scoring using fine-grained payment data," Working Papers 2017011, University of Antwerp, Faculty of Business and Economics.

    More about this item


    Bipartite graphs; Two-mode networks; Affiliation networks; Node classification; Big data;

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