2008/01/21 by Kaplan, Todd D., Forrest, Stephanie
#Data Analysis #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.0801.3290
Current community detection algorithms operate by optimizing a statistic called modularity, which analyzes the distribution of positively weighted edges in a network. Modularity does not account for negatively weighted edges. This paper introduces a dual assortative modularity measure (DAMM) that incorporates both positively and negatively weighted edges. We describe the the DAMM statistic and illustrate its utility in a community detection algorithm. We evaluate the efficacy of the algorithm on both computer generated and real-world networks, showing that DAMM broadens the domain of networks that can be analyzed by community detection algorithms.