Mapping directed networks

Crofts, J.J. ORCID: 0000-0001-7751-9984, Estrada, E., Higham, D.J. and Taylor, A., 2010. Mapping directed networks. Electronic Transactions in Numerical Analysis, 37, pp. 337-350. ISSN 1068-9613

[img]
Preview
Text
205849_PubSub711_Crofts_pre.pdf

Download (1MB) | Preview

Abstract

We develop and test a new mapping that can be applied to directed unweighted networks. Although not a "matrix function" in the classical matrix theory sense, this mapping converts an unsymmetric matrix with entries of zero or one into a symmetric real-valued matrix of the same dimension that generally has both positive and negative entries. The mapping is designed to reveal approximate directed bipartite communities within a complex directed network; each such community is formed by two set of nodes S 1 and S 2 such that the connections involving these nodes are predominantly from a node in S 1 and to a node in S 2 . The new mapping is motivated via the concept of alternating walks that successively respect and then violate the orientations of the links. Considering the combinatorics of these walks leads us to a matrix that can be neatly expressed via the singular value decomposition of the original adjacency matrix and hyperbolic functions. We argue that this new matrix mapping has advantages over other, exponential-based measures. Its performance is illustrated on synthetic data, and we then show that it is able to reveal meaningful directed bipartite substructure in a network from neuroscience.

Item Type: Journal article
Publication Title: Electronic Transactions in Numerical Analysis
Creators: Crofts, J.J., Estrada, E., Higham, D.J. and Taylor, A.
Publisher: Kent State University
Date: 2010
Volume: 37
ISSN: 1068-9613
Rights: Copyright © 2010 Kent State University
Divisions: Schools > School of Science and Technology
Depositing User: EPrints Services
Date Added: 09 Oct 2015 10:54
Last Modified: 09 Jun 2017 13:44
URI: http://irep.ntu.ac.uk/id/eprint/19841

Actions (login required)

Edit View Edit View

Views

Views per month over past year

Downloads

Downloads per month over past year