• Betweenness centrality

  A divide-and-conquer approach based on Brandes' algorithm for computing betweenness centrality. Our modified algorithm is very efficient in graphs that exhibit certain clustering structure.
  
  If you use this code please cite:
  Dora Erdos, Vatche Ishakian, Azer Bestavros, Evimaria Terzi, A Divide-and-Conquer Algorithm for Betweenness Centrality, SIAM Data Mining Conference,2015, Vancouver, Canada
  
  README
  brandespp.zip
  

  Reconstructing graphs from neighborhood data

  We study the reconstruction of a hidden bipartite graph, when only information on the number of common neighbors between nodes is given.
  
  For this code please cite:
  Dora Erdos, Rainer Gemulla, Evimaria Terzi, Reconstructing Graphs from Neighborhood Data, ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 8 Issue 4, Article No. 23, ACM New York, NY, USA, August 2014
  
  README
  example_graph.zip
  recSVD.zip
  iterative.zip
  LS.zip
  hybrid.zip

  Filter placement

  A greedy heuristic for filter placement in information propagation networks to reduce information multiplicity.
  
  If you use this code please cite:
  Dora Erdos, Vatche Ishakian, Andrei Lapets, Evimaria Terzi, Azer Bestavros, The Filter Placement Problem and its Application to Minimizing Information Multiplicity, International Conference on Very Large DataBases (VLDB), Istanbul, Turkey, August 2012
  
  README
  filters.py
  toy example