The stochastic block-model and its non-parametric extension, the Infinite Relational Model (IRM), have become key tools for discovering group-structure in complex networks. Identifying these groups is a combinatorial inference problem which is usually solved by Gibbs sampling. However, whether Gibbs sampling suffices and can be scaled to the modeling of large scale real world complex networks has not been examined sufficiently. In this paper we evaluate the performance and mixing ability of Gibbs sampling in the Infinite Relational Model (IRM) by implementing a high performance Gibbs sampler. We find that Gibbs sampling can be computationally scaled to handle millions of nodes and billions of links. Investigating the behavior of the Gibbs sampler for different sizes of networks we find that the mixing ability decreases drastically with the network size, clearly indicating a need for better sampling strategies.
Machine Learning for Signal Processing, 2013
Bioengineering; Communication, Networking and Broadcast Technologies; Computing and Processing; General Topics for Engineers; Robotics and Control Systems; Signal Processing and Analysis; Transportation
Main Research Area:
Machine Learning for Signal Processing
2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013