DynamicNet, an effective and efficient algorithm for supporting community evolution detection in time-evolving information networks is presented and experimentally evaluated in this paper. DynamicNet introduces a graph-based model-theoretic approach to represent time-evolving information networks, and to capture how they change over time. A central feature of DynamicNet is represented by the ability of supporting matching-based community evolution detection, by identifying several classes of community transitions. Experimental results clearly demonstrate the reliability and the efficiency of our proposal. © 2013 ACM.
DynamicNet: An effective and efficient algorithm for supporting community evolution detection in time-evolving information networks
Cuzzocrea A;Folino F;Pizzuti C
2013
Abstract
DynamicNet, an effective and efficient algorithm for supporting community evolution detection in time-evolving information networks is presented and experimentally evaluated in this paper. DynamicNet introduces a graph-based model-theoretic approach to represent time-evolving information networks, and to capture how they change over time. A central feature of DynamicNet is represented by the ability of supporting matching-based community evolution detection, by identifying several classes of community transitions. Experimental results clearly demonstrate the reliability and the efficiency of our proposal. © 2013 ACM.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
