$\text{RD}\small{\text{YN}}$: graph benchmark handling community dynamics. (5th July 2017)

• Record Type: Journal Article
• Authors: Rossetti, Giulio
• Is Part Of: Journal of complex networks. Volume 5:Number 6(2017)
• Page Start: 893
• Page End: 912
• Publication Date: 2017-07-05
• Subjects: graph models -- dynamic networks -- evolving communities; Numerical analysis -- Periodicals; Computer networks -- Periodicals; Social networks -- Periodicals; 518.05
• DOI: 10.1093/comnet/cnx016
• Languages: English
• ISSNs: 2051-1310

Abstract

Abstract: 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 $\text{RD}\small{\text{YN}}$, an approach able to generates dynamic networks along with time-dependent ground-truth partitions having tunable quality.