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Nothing yet from Complexity digest this week, but here's the abs of a new 
article from the new Social Networks & Mining journal


  Barry Wellman
  _______________________________________________________________________

   S.D. Clark Professor of Sociology, FRSC               NetLab Director
   Department of Sociology                  725 Spadina Avenue, Room 388
   University of Toronto   Toronto Canada M5S 2J4   twitter:barrywellman
   http://www.chass.utoronto.ca/~wellman             fax:+1-416-978-3963
   Updating history:      http://chass.utoronto.ca/oldnew/cybertimes.php
  _______________________________________________________________________

Social Network Analysis and Mining
DOI: 10.1007/s13278-011-0021-0Online First™

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

Katharina Anna Zweig and Michael Kaufmann


     * Volume 1
           o 73-142Number 2 / April 2011
           o 1-72Number 1 / January 2011


Abstract
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 v, w, 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.

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