Micro-Community Detection and Vulnerability Identification for Large Critical Networks
Document Type
Conference Proceeding
Publication Date
1-1-2016
Publication Title
2016 IEEE Symposium on Technologies for Homeland Security, HST 2016
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
In this work we put forward our novel approach using graph partitioning and Micro-Community detection techniques. We firstly use algebraic connectivity or Fiedler Eigenvector and spectral partitioning for community detection. We then used modularity maximization and micro level clustering for detecting micro-communities with concept of community energy. We run micro-community clustering algorithm recursively with modularity maximization which helps us identify dense, deeper and hidden community structures. We experimented our MicroCommunity Clustering (MCC) algorithm for various types of complex technological and social community networks such as directed weighted, directed unweighted, undirected weighted, undirected unweighted. A novel fact about this algorithm is that it is scalable in nature. © 2016 IEEE.
Keywords
Community Detection; Eigenvalue; Eigenvector; Large Networks; Vulnerability
Language
English
Repository Citation
Chopade, P.,
Zhan, J.,
Bikdash, M.
(2016).
Micro-Community Detection and Vulnerability Identification for Large Critical Networks.
2016 IEEE Symposium on Technologies for Homeland Security, HST 2016
Institute of Electrical and Electronics Engineers Inc..
http://dx.doi.org/10.1109/THS.2016.7568930