Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
Computational & Mathematical Organization Theory
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Extended Generalized Blockmodeling for Compound Communities and External Actors
CASON '09 Proceedings of the 2009 International Conference on Computational Aspects of Social Networks
Grouping genetic algorithm for the blockmodel problem
IEEE Transactions on Evolutionary Computation
Improved Bayesian inference for the stochastic block model with application to large networks
Computational Statistics & Data Analysis
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In recent years, the summarisation and decomposition of social networks has become increasingly popular, from community finding to role equivalence. However, these approaches concentrate on one type of model only. Generalised blockmodelling decomposes a network into independent, interpretable, labeled blocks, where the block labels summarise the relationship between two sets of users. Existing algorithms for fitting generalised blockmodels do not scale beyond networks of 100 vertices. In this paper, we introduce two new algorithms, one based on genetic algorithms and the other on simulated annealing, that is at least two orders of magnitude faster than existing algorithms and obtaining similar accuracy. Using synthetic and real datasets, we demonstrate their efficiency and accuracy and show how generalised block-modelling and our new approaches enable tractable network summarisation and modelling of medium sized networks.