Name
Abstract | ||
|---|---|---|
Time-varying networks describe a wide array of systems whose constituents and interactions evolve over time. They are defined by an ordered stream of interactions between nodes, yet they are often represented in terms of a sequence of static networks, each aggregating all edges and nodes present in a time interval of size Delta t. In this work we quantify the impact of an arbitrary Delta t on the description of a dynamical process taking place upon a time-varying network. We focus on the elementary random walk, and put forth a simple mathematical framework that well describes the behavior observed on real datasets. The analytical description of the bias introduced by time integrating techniques represents a step forward in the correct characterization of dynamical processes on time-varying graphs. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1038/srep03006 | SCIENTIFIC REPORTS |
Keywords | Field | DocType |
bioinformatics,biomedical research | Data mining,Graph,Random walk,Computer science,Theoretical computer science,Temporal resolution | Journal |
Volume | ISSN | Citations |
3 | 2045-2322 | 20 |
PageRank | References | Authors |
0.85 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruno F. Ribeiro | 1 | 572 | 41.35 |
| Nicola Perra | 2 | 21 | 1.91 |
| Andrea Baronchelli | 3 | 465 | 45.58 |