SeqiBloc: mining multi-time spanning blockmodels in dynamic graphs

Blockmodelling is an important technique for decomposing graphs into sets of roles. Vertices playing the same role have similar patterns of interactions with vertices in other roles. These roles, along with the role to role interactions, can succinctly summarise the underlying structure of the studied graphs. As the underlying graphs evolve with time, it is important to study how their blockmodels evolve too. This will enable us to detect role changes across time, detect different patterns of interactions, for example, weekday and weekend behaviour, and allow us to study how the structure in the underlying dynamic graph evolves. To date, there has been limited amount of work and techniques studying dynamic blockmodels. They focus on smoothing role changes between adjacent time instances. However, this approach can overfit during stationary periods where the underling structure doesn’t change but there are random noise in the graph. Therefore, an approach to a) find blockmodels ac...

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Jeffrey Chan, Wei Liu, Christopher Leckie, James Bailey, Kotagiri Ramamohanarao
• Keywords: Data Mining, Detection Approach, Dynamic Graph, KDD 2012, Role Changes