Name
Abstract | ||
|---|---|---|
Most instruments - formalisms, concepts, and metrics - for social networks
analysis fail to capture their dynamics. Typical systems exhibit different
scales of dynamics, ranging from the fine-grain dynamics of interactions (which
recently led researchers to consider temporal versions of distance,
connectivity, and related indicators), to the evolution of network properties
over longer periods of time. This paper proposes a general approach to study
that evolution for both atemporal and temporal indicators, based respectively
on sequences of static graphs and sequences of time-varying graphs that cover
successive time-windows. All the concepts and indicators, some of which are
new, are expressed using a time-varying graph formalism. |
Year | Keywords | Field |
|---|---|---|
2011 | artificial intelligent,social network analysis,cluster computing,dynamic range | Dynamic network analysis,Graph,Data mining,Social network,Computer science,Social network analysis,Theoretical computer science,Ranging,Artificial intelligence,Formalism (philosophy),Rotation formalisms in three dimensions,Machine learning |
DocType | Volume | Citations |
Journal | abs/1102.0 | 28 |
PageRank | References | Authors |
1.22 | 22 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Santoro | 1 | 1145 | 55.49 |
| Walter Quattrociocchi | 2 | 582 | 42.16 |
| Paola Flocchini | 3 | 2421 | 157.13 |
| Arnaud Casteigts | 4 | 406 | 27.35 |
| Frédéric Amblard | 5 | 430 | 51.43 |