Group Detection in Real-world Social Networks

Group detection is of fundamental importance in many social network applications. Although considerable methods have been presented to address the task, the objective of seeking a good trade-off between effectiveness and efficiency remains challenging. This paper proposes a method named as R_Acut to meet the challenging objective. R_Acut adopts random walk-based heuristic and Average cut (Acut) to make the algorithm efficient and is capable of giving effective solutions. The performance of R_Acut is validated through comparisons with other representative methods against both synthetic and real-world networks of different sizes.