Paper Info
Title
On Joint Diagonalisation for Dynamic Network Analysis
Abstract
Joint diagonalisation (JD) is a technique used to estimate an average eigenspace of a set of matrices. Whilst it has been used successfully in many areas to track the evolution of systems via their eigenvectors; its application in network analysis is novel. The key focus in this paper is the use of JD on matrices of spanning trees of a network. This is especially useful in the case of real-world contact networks in which a single underlying static graph does not exist. The average eigenspace may be used to construct a graph which represents the `average spanning tree' of the network or a representation of the most common propagation paths. We then examine the distribution of deviations from the average and find that this distribution in real-world contact networks is multi-modal; thus indicating several \emph{modes} in the underlying network. These modes are identified and are found to correspond to particular times. Thus JD may be used to decompose the behaviour, in time, of contact networks and produce average static graphs for each time. This may be viewed as a mixture between a dynamic and static graph approach to contact network analysis.
Year
Venue
Keywords
2011
CoRR
eigenvectors,network analysis,spanning tree
Field
DocType
Volume
Dynamic network analysis,Graph,Matrix (mathematics),Computer science,Artificial intelligence,Spanning tree,Network analysis,Machine learning,Eigenvalues and eigenvectors
Journal
abs/1110.1198
Citations 
PageRank 
References 
0
0.34
10
Authors
3
Name
Order
Citations
PageRank
Damien Fay113514.41
Jérôme Kunegis287451.20
Eiko Yoneki3160594.23