Nonnegative matrix factorization to find features in temporal networks
- Resource Type
- Conference
- Authors
- Hamon, Ronan; Borgnat, Pierre; Flandrin, Patrick; Robardet, Celine
- Source
- 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. :1065-1069 May, 2014
- Subject
- Signal Processing and Analysis
Communities
Matrix decomposition
Time-frequency analysis
Visualization
Time series analysis
Labeling
Evolution (biology)
nonnegative matrix factorization
temporal networks
Fourier analysis
dynamic graphs
multidimensional scaling
- Language
- ISSN
- 1520-6149
2379-190X
Temporal networks describe a large variety of systems having a temporal evolution. Characterization and visualization of their evolution are often an issue especially when the amount of data becomes huge. We propose here an approach based on the duality between graphs and signals. Temporal networks are represented at each time instant by a collection of signals, whose spectral analysis reveals connection between frequency features and structure of the network. We use nonnegative matrix factorization (NMF) to find these frequency features and track them over time. Transforming back these features into subgraphs reveals the underlying structures which form a decomposition of the temporal network.