TY - GEN
A1 - Willenbockel, Christian Tobias
A1 - SchÃ¼tte, Christof
T1 - Variational Bayesian Inference and Model Selection for the Stochastic Block Model with Irrelevant Vertices
N2 - Real World networks often exhibit a significant number of vertices which are sparsely and irregularly connected to other vertices in the network. For clustering theses networks with a model based algorithm, we propose the Stochastic Block Model with Irrelevant Vertices (SBMIV) for weighted net- works. We propose an original Variational Bayesian Expectation Maximiza- tion inference algorithm for the SBMIV which is an advanced version of our Blockloading algorithm for the Stochastic Block Model. We introduce a model selection criterion for the number of clusters of the SBMIV which is based on the lower variational bound of the model likelihood. We propose a fully Bayesian inference process, based on plausible informative priors, which is independent of other algorithms for preprocessing start values for the cluster assignment of vertices. Our inference methods allow for a multi level identification of irrelevant vertices which are hard to cluster reliably ac- cording to the SBM. We demonstrate that our methods improve on the normal Stochastic Block model by applying it to to Earthquake Networks which are an example of networks with a large number of sparsely and irregularly con- nected vertices.
T3 - ZIB-Report - 16-01
KW - Clustering
KW - Variational Bayes EM
KW - Model Selection,
KW - Stochastic Block Model
KW - Networks
KW - unsupervised classification
KW - Noise
Y1 - 2016
UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/5700
UR - https://nbn-resolving.org/urn:nbn:de:0297-zib-57003
SN - 1438-0064
ER -