Shifting Niches for Community Structure Detection
Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings
We present a new evolutionary algorithm for com- munity structure detection in both undirected and unweighted (sparse) graphs and fully connected weighted digraphs (complete networks). Previous investigations have found that, although evolutionary computation can identify community structure in complete networks, this approach seems to scale badly due to solutions with the wrong number of communities dominating the population. The new algorithm is based on a niching model, where separate compartments of the population contain candidate solutions with different numbers of communities. We experimentally compare the new algorithm to the well-known algorithms of Pizzuti and Tasgin, and find that we outperform those algorithms for sparse graphs under some conditions, and drastically outperform them on complete networks under all tested conditions.
|Title of host publication||Evolutionary Computation (CEC), 2013 IEEE Congress on|
|Number of pages||8|
|Publisher||IEEE Computer Society Press|
|Publication date||21 Jun 2013|
|State||Published - 21 Jun 2013|