Title

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

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