Events

Date: 21 May 2026   Time: 14:00 - 15:00     

Location: Hybrid: MB503, SMS, QMUL, or via the Teams link below

Spectral clustering has been used widely as a popular tool for community detection in data with network structure. However, spectral clustering does not perform well on certain network structures, particularly core-periphery networks. To improve clustering performance in core-periphery structures, Adjacency Spectral Embedding (ASE) has been introduced. Despite its advantages, ASE has several limitations including its optimal performance only on dense networks. To address these limitations, we proposed a new approach called Doubled Adjacency Spectral Embedding (DASE). We demonstrate that DASE overcomes these challenges, particularly highlighting the improved clustering performance on both sparse and dense networks in the presence of core-periphery structures.