7 research outputs found
Bi-Objective Community Detection (BOCD) in Networks using Genetic Algorithm
A lot of research effort has been put into community detection from all
corners of academic interest such as physics, mathematics and computer science.
In this paper I have proposed a Bi-Objective Genetic Algorithm for community
detection which maximizes modularity and community score. Then the results
obtained for both benchmark and real life data sets are compared with other
algorithms using the modularity and MNI performance metrics. The results show
that the BOCD algorithm is capable of successfully detecting community
structure in both real life and synthetic datasets, as well as improving upon
the performance of previous techniques.Comment: 11 pages, 3 Figures, 3 Tables. arXiv admin note: substantial text
overlap with arXiv:0906.061
Mining Scale-free Networks using Geodesic Clustering
Many real-world graphs have been shown to be scale-free--- vertex degrees follow power law distributions, vertices tend to cluster, and the average length of all shortest paths is small. We present a new model for understanding scale-free networks based on multilevel geodesic approximation, using a new data structure called a multilevel mesh. Using thi