# Communities

## Required readings

## Further readings

- T.S. Evans and R. Lambiotte, Line graphs, link partitions, and overlapping communities,
*PRE*, 2009 - Independently developed line-graph based approach.
- T.S. Evans, Clique graphs and overlapping communities,
*Journal of Statistical Mechanics*, 2010 - Higher order generalization
- I. Derény et al., Clique percolation in random networks,
*PRL*, 2005 - Analytical study on the properties of clique percolation in random networks.
- J.M. Kumpula et al., A sequential algorithm for fast clique percolation,
*PRE*, 2008 - An improved algorithm of clique percolation. Also provide natural generalization to weighted networks.
- Python implemention of the SCP algorithm - Python source code for the sequential algorithm.
- S. Lehmann et al., Bi-clique communities
*PRE*, 2008 - Extension of clique percolation to bi-partite graphs
- C. Lee et al., Detecting highly overlapping community structure by greedy clique expansion,
*arXiv*, 2010 - Another approach based on cliques.

## Questions

- Why should we care about ‘overlapping communities’?
- What is a ‘clique’ and what is a ‘k-clique’?
- What is the definition of a community in the Clique Percolation Method? Why do they define it this way?
- What’s the drawbacks of CPM?
- Why community overlap and hierarchical structure are incongruent in node-community paradigm?
- What are the benefits of ‘link community’ concept? Any drawbacks?
- What is ‘line graph transformation’? What are the problems of simple line graph transformation? How does link community method improve?
- What is the main idea of ‘generative modeling framework’? How is it related to the maximum-likelihood framework?