|$\text{RD}\small{\text{YN}}$| ⁠: graph benchmark handling community dynamics

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.