Detecting Overlapping Link Communities by Finding Local Minima of a Cost Function with a Memetic Algorithm. Part 1: Problem and Method
Social and Information Networks
2016-06-24 v2 Physics and Society
Abstract
We propose an algorithm for detecting communities of links in networks which uses local information, is based on a new evaluation function, and allows for pervasive overlaps of communities. The complexity of the clustering task requires the application of a memetic algorithm that combines probabilistic evolutionary strategies with deterministic local searches. In Part 2 we will present results of experiments with with citation networks.
Cite
@article{arxiv.1501.05139,
title = {Detecting Overlapping Link Communities by Finding Local Minima of a Cost Function with a Memetic Algorithm. Part 1: Problem and Method},
author = {Frank Havemann and Jochen Gläser and Michael Heinz},
journal= {arXiv preprint arXiv:1501.05139},
year = {2016}
}
Comments
11 pages, 2 figures, 2 appendixes; there is some overlap in Appendix A with our earlier preprint arXiv:1206.3992; we have added a reference and revised arguments in sections 1, 3, and 4