Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/3805
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dc.contributor.authorRui Portocarrero Sarmentoen
dc.contributor.authorMário Miguel Cordeiroen
dc.contributor.authorJoão Gamaen
dc.date.accessioned2017-11-23T11:44:49Z-
dc.date.available2017-11-23T11:44:49Z-
dc.date.issued2015en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/3805-
dc.identifier.urihttp://dx.doi.org/10.1145/2695664.2695960en
dc.description.abstractLarge scale social networks streaming and visualization has been a hot topic in recent research. Researchers strive to achieve efficient streaming methods and to be able to gather knowledge from the results. Moreover treating the data as a continuous real time flow is a demand for immediate response to events in daily life. Our contribution is to treat the data as a continuous stream and represent it by streaming the egocentric networks (Ego-Networks) for particular nodes. We propose a non-standard node forgetting factor in the representation of the network data stream. Thus, this representation is sensible to recent events in users networks and less sensible for the past node events. The aim of these techniques is the visualization of large scale Ego-Networks from telecommunications social networks with power law distributions.en
dc.languageengen
dc.relation5880en
dc.relation5120en
dc.relation4223en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleVisualization of Evolving Large Scale Ego-Networksen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:LIAAD - Indexed Articles in Conferences

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