Faculty Scholarly Dissemination Grants
Enumerating Communities for a Deeper Understanding of Community Finding
Department
School of Computing and Information Systems
College
Padnos College of Engineering and Computing
Date Range
2013-2014
Disciplines
Engineering
Abstract
To advance the current state of the practice of community finding within networks, this paper presents algorithms for exploring the range of possible assignments of nodes to communities. One algorithm is an efficient method for enumerating the assignments. The second algorithm is an unranking of assignments, i.e. it is an algorithm for generating the assignment corresponding to a given rank in the numbering of assignments. Both of these algorithms are parallelizable. The unranking algorithm can also be used to generate sample assignments (e.g. for Monte Carlo) by using random rank numbers. The following discoveries we have made using these algorithms have important ramifications for the practice of community finding: 1) the distribution of metrics over the range of assignments appears to be normal, 2) the curve for finding the best number of communities is almost always U shaped, and 3) it is likely that the best solutions for a network will have varied characteristics.
Conference Name
International Conference on Web Intelligence
Conference Location
Warsaw Poland
ScholarWorks Citation
Kurmas, Dr. Zachary; Scripps, Jerry; McGuire, Hugh; and Trefftz, Christian, "Enumerating Communities for a Deeper Understanding of Community Finding" (2014). Faculty Scholarly Dissemination Grants. 863.
https://scholarworks.gvsu.edu/fsdg/863