Darren Narayan, RIT, 332 Phillips Hall, 4:00 pm

Title:  Refining the clustering coefficient for analysis of social and neural network data

Abstract: We will show how a deeper analysis of the clustering coefficient in a network can be used to assess functional connections in the human brain. Our metric of clustering centrality considers the frequency at which an edge appears across all local subgraphs that are induced by each vertex and its neighbors. This analysis is tied to a problem from structural graph theory in which we seek the largest subgraph that is a Cartesian product of two complete bipartite graphs K_{1,m} and K_{1,1}. We investigate this property and compare it to other known edge centrality metrics. Finally, we apply the property of clustering centrality to an analysis of functional MRI data from the Rochester Center for Brain Imaging at the University of Rochester.