Neighborhood-based Bridge Node Centrality Tuple for Complex Network Analysis
Speaker: Natarajan Meghanathan, Jackson State University
Abstract: We define a bridge node to be a node whose neighbor nodes are sparsely connected and are likely to be part of different components/disjoint clusters if the node is removed from the network. We propose a computationally light Neighborhood-based Bridge Node Centrality (NBNC) tuple that could be used to identify the bridge nodes of a network as well as rank the nodes in a network based on their topological position to function as bridge nodes. The NBNC tuple for a node is asynchronously computed based on the neighborhood graph of the node that comprises the neighbors of the node as well as the vertices and the links connecting the neighbors as edges. The NBNC tuple for a node has three entries: the number of disjoint components in the neighborhood graph of the node, the algebraic connectivity ratio of the neighborhood graph of the node and the number of neighbors of the node. We analyze a suite of 50 complex real-world networks and evaluate the computational lightness, effectiveness, efficiency/accuracy, and uniqueness of the NBNC tuple vis-a-vis the existing bridgeness-related centrality metrics and the Louvain community detection algorithm. From a computational epidemiology point of view, the NBNC tuple could be used to effectively and efficiently identify bridge nodes that need to be vaccinated in a social network or organizational network to provide herd immunity to nodes that are not vaccinated.
Bio: Dr. Natarajan Meghanathan is a tenured Full Professor of Computer Science at Jackson State University, Jackson, Mississippi. He graduated with a Ph.D. in Computer Science from the University of Texas at Dallas in May 2005. Dr. Meghanathan has published more than 150 peer-reviewed articles (with more than half of them as journal publications). He has also received federal education and research grants from the U.S. National Science Foundation, Army Research Lab, and Air Force Research Lab. Dr. Meghanathan has been serving on the editorial board of several international journals and the Technical Program Committees and Organization Committees of several international conferences. His research interests are Wireless Ad hoc Networks and Sensor Networks, Systems and Software Security, Graph Theory Algorithms, Machine Learning, Cloud Computing, and Computational Biology.