Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks
arXiv:1906.04840 · doi:10.1007/978-3-030-23495-9_3
Abstract
We recently introduced a formalism for the modeling of temporal networks, that we call stream graphs. It emphasizes the streaming nature of data and allows rigorous definitions of many important concepts generalizing classical graphs. This includes in particular size, density, clique, neighborhood, degree, clustering coefficient, and transitivity. In this contribution, we show that, like graphs, stream graphs may be extended to cope with bipartite structures, with node and link weights, or with link directions. We review the main bipartite, weighted or directed graph concepts proposed in the literature, we generalize them to the cases of bipartite, weighted, or directed stream graphs, and we show that obtained concepts are consistent with graph and stream graph ones. This provides a formal ground for an accurate modeling of the many temporal networks that have one or several of these features.
References in corpus (18)
- The architecture of complex weighted networks
- Random graphs with arbitrary degree distributions and their applications
- Who is the best connected scientist? A study of scientific coauthorship networks
- Analysis of weighted networks
- Intensity and coherence of motifs in weighted complex networks
- Extracting the multiscale backbone of complex weighted networks
- Clustering in Complex Directed Networks
- Generalizations of the clustering coefficient to weighted complex networks
- Prominence and control: The weighted rich-club effect
- Cycles and clustering in bipartite networks
- The clustering coefficient and community structure of bipartite networks
- Stream Graphs and Link Streams for the Modeling of Interactions over Time
- Directed clustering in weighted networks: a new perspective
- On the rich-club effect in dense and weighted networks
- A Unifying Framework for Measuring Weighted Rich Clubs
- An ensemble approach to the analysis of weighted networks
- On MultiAspect Graphs
- Cluster-Span Threshold: An unbiased threshold for binarising weighted complete networks in functional connectivity analysis