Conference Proceedings

Improving Single and Multi-View Blockmodelling by Algebraic Simplification

R Ramteke, PJ Stuckey, J Chan, K Ramamohanarao, J Bailey, C Leckie, E Demirovic

2020 International Joint Conference on Neural Networks (IJCNN) | IEEE | Published : 2020


Blockmodelling is an important technique in social network analysis for discovering the latent structures and groupings in graphs. State-of-the-art approaches approximate the graph using matrix factorisation, which can discover both the latent graph structures and vertex groupings. However, factorisation is a one-way approximation, in that it only approximates the graph with a lossy model that removes the background noise. Traditional Blockmodelling methods rely on an alternating 2-step optimization that involves iteratively updating the matrix representing membership while fixing the matrix representing the graph's underlying structure, and then updating the structure matrix while keeping t..

View full abstract


Awarded by Australian Research Council

Funding Acknowledgements

This research was supported (partially or fully) by the Australian Government through the Australian Research Council's Discovery Projects funding scheme (project DP170103174).