Social Network Analysis and Mining

, Volume 1, Issue 3, pp 187–218

A systematic approach to the one-mode projection of bipartite graphs

Original Article

DOI: 10.1007/s13278-011-0021-0

Cite this article as:
Zweig, K.A. & Kaufmann, M. Soc. Netw. Anal. Min. (2011) 1: 187. doi:10.1007/s13278-011-0021-0


Bipartite graphs are common in many complex systems as they describe a relationship between two different kinds of actors, e.g., genes and proteins, metabolites and enzymes, authors and articles, or products and consumers. A common approach to analyze them is to build a graph between the nodes on one side depending on their relationships with nodes on the other side; this so-called one-mode projection is a crucial step for all further analysis but a systematic approach to it was lacking so far. Here, we present a systematic approach that evaluates the significance of the co-occurrence for each pair of nodes vw, i.e., the number of common neighbors of v and w. It turns out that this can be seen as a special case of evaluating the interestingness of an association rule in data mining. Based on this connection we show that classic interestingness measures in data mining cannot be applied to evaluate most real-world product-consumer relationship data. We thus introduce generalized interestingness measures for both, one-mode projections of bipartite graphs and data mining and show their robustness and stability by example. We also provide theoretical results that show that the old method cannot even be used as an approximative method. In a last step we show that the new interestingness measures show stable and significant results that result in attractive one-mode projections of bipartite graphs.


Bipartite graphs One-mode projection Association rules Interestingness measures 

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Interdisciplinary Center for Scientific Computing, University of HeidelbergHeidelbergGermany
  2. 2.Wilhelm-Schickard-Institute, University of TübingenTübingenGermany

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