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

Abstract

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..

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Grants

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).