Title :
An Agglomerative Method to Construct Discrepant Cohesive Subgroups
Author :
Hecking, Tobias ; Gohnert, Tilman ; Hoppe, H. Ulrich
Author_Institution :
Dept. of Comput. Sci. & Appl. Cognitive Sci., Univ. of Duisburg-Essen, Essen, Germany
Abstract :
This paper introduces an agglomerative method for detecting cohesive subgroups in networks based on geodesic distance. The algorithm starts with a set of nodes as "seed". Beginning with the seed nodes as initial clusters, the clusters grow by incorporating more nodes successively based on minimal average distance to the current members of the cluster as a criterion for cluster extension. This approach is combined with an optimization step to achieve high quality performance on subgroup detection. The resulting method for detecting discrepant cohesive subgroups has been tested on artificial benchmark graphs as well as real-world networks.
Keywords :
graph theory; pattern clustering; social sciences; agglomerative method; artificial benchmark graph; cluster extension; cohesive subgroup detection; discrepant cohesive subgroup; geodesic distance; real-world network; seed node; Benchmark testing; Clustering algorithms; Communities; Educational institutions; Optimization; Physics; Social network services; cohesive subgroup detection; community detection; social network analysis;
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4673-2497-7
DOI :
10.1109/ASONAM.2012.125
