Networks are data structures more and more frequently used for modeling interactions in social and biological phenomena, as well as between various types of devices, tools and machines. They can be either static or dynamic, dependently on whether the modeled interactions are fixed or changeable over time. Static networks have been extensively investigated in data mining, while fewer studies have focused on dynamic networks and how to discover complex patterns in large, evolving networks. In this paper we focus on the task of discovering changes in evolving networks and we overcome some limits of existing methods (i) by resorting to a relational approach for representing networks characterized by heterogeneous nodes and/or heterogeneous relationships, and (ii) by proposing a novel algorithm for discovering changes in the structure of a dynamic network over time. Experimental results and comparisons with existing approaches on real-world datasets prove the effectiveness and efficiency of the proposed solution and provide some insights on the effect of some parameters in discovering and modeling the evolution of the whole network, or a subpart of it.

Relational mining for discovering changes in evolving networks

LOGLISCI, CORRADO;CECI, MICHELANGELO;MALERBA, Donato
2015

Abstract

Networks are data structures more and more frequently used for modeling interactions in social and biological phenomena, as well as between various types of devices, tools and machines. They can be either static or dynamic, dependently on whether the modeled interactions are fixed or changeable over time. Static networks have been extensively investigated in data mining, while fewer studies have focused on dynamic networks and how to discover complex patterns in large, evolving networks. In this paper we focus on the task of discovering changes in evolving networks and we overcome some limits of existing methods (i) by resorting to a relational approach for representing networks characterized by heterogeneous nodes and/or heterogeneous relationships, and (ii) by proposing a novel algorithm for discovering changes in the structure of a dynamic network over time. Experimental results and comparisons with existing approaches on real-world datasets prove the effectiveness and efficiency of the proposed solution and provide some insights on the effect of some parameters in discovering and modeling the evolution of the whole network, or a subpart of it.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/126738
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