Version 1.3

Update:
04/08: Fixed output of encoding cost. Added overlap encoding for structures.

Code for 
   VoG: Summarizing and Understanding Large Graphs
   Danai Koutra, U Kang, Jilles Vreeken, and Christos Faloutsos
   http://www.cs.cmu.edu/~dkoutra/papers/VoG.pdf

   Overlapping encoding:
   An Empirical Comparison of the Summarization Power of Graph Clustering Methods
   Yike Liu, Neil Shah, Danai Koutra
   http://arxiv.org/abs/1511.06820


Contact:
   Danai Koutra, dkoutra@umich.edu
   Yike Liu, yikeliu@umich.edu

To run:
   type 'make'


Difference from Version 1.0:
   Using dynamic programming and the technique of memoization to 
   speed up the application of the GREEDY'nFORGET heuristic.


Algorithm:

Input: graph G
Step 1: Subgraph Generation. Generate candidate – possibly
overlapping – subgraphs using one or more graph decomposition
methods.
Step 2: Subgraph Labeling. Characterize each subgraph as a
perfect structure x \in Omega, or an approximate structure by using
MDL to find the type x that locally minimizes the encoding cost.
Populate the candidate set C.
Step 3: Summary Assembly. Use the heuristics PLAIN, TOP10,
TOP100, GREEDY’NFORGET (Sec. 4.3) to select a non-redundant
subset from the candidate structures to instantiate the graph model
M. Pick the model of the heuristic with the lowest description
cost.
Return graph summary M and its encoding cost.



Change Log:
===========

July 1, 2015
- removed vpi():  using l2cnk.m to compute the log of n-choose-k efficiently
  leads to 30x speedup in the chocolate-wiki dataset
- tic/toc instead of cputime to compute the runtime: following the recommendation at http://www.mathworks.com/help/matlab/ref/cputime.html

January 9, 2015
- Replaced the config.py file

July 30,  2014
- Fixed ordering of nodes in cliques

June 15, 2014
- Made the greedyNforget 100x faster by exploiting memoization