@techreport{eprints2118,
          author = {Nicola Santoro and Walter Quattrociocchi and Paola Flocchini and Arnaud Casteigts and Frederic Amblard},
     institution = {IMT Institute for Advanced Studies Lucca},
            type = {Working Paper},
       publisher = {ArXiv},
            year = {2011},
           title = {Time-varying graphs and social network analysis: temporal indicators and metrics},
        keywords = {Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Physics and Society (physics.soc-ph)},
             url = {http://eprints.imtlucca.it/2118/},
        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. }
}