RDyn: graph benchmark handling community dynamics

Rossetti G.

Dynamic networks  Management Science and Operations Research  Computational Mathematics  Control and Optimization  Generators  Computer Networks and Communications  Applied Mathematics  Community 

Graph models provide an understanding of the dynamics of network formation and evolution; as a direct consequence, synthesizing graphs having controlled topology and planted partitions has been often identified as a strategy to describe benchmarks able to assess the performances of community discovery algorithm. However, one relevant aspect of real-world networks has been ignored by benchmarks proposed so far: community dynamics. As time goes by network communities rise, fall and may interact with each other generating merges and splits. Indeed, during the last decade dynamic community discovery has become a very active research field: in order to provide a coherent environment to test novel algorithms aimed at identifying mutable network partitions we introduce RDYN, an approach able to generates dynamic networks along with time-dependent ground-truth partitions having tunable quality.

Source: Journal of complex networks (Online) 5 (2017): 893–912. doi:10.1093/comnet/cnx016

Publisher: Oxford University Press, Oxford, Regno Unito

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