Scalable Inference of Overlapping Communities

118 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Efficient discovery of overlapping communities in massive networks (2013, background): “Using these strategies can lead to faster convergence of the variational distribution (34).”
Spectral redemption in clustering sparse networks (2013, background): “[33] Gopalan P, Mimno D, Gerrish S, Freedman M and Blei D (2012).”
Advances in Variational Inference (2017, background): “Several authors suggested variants of importance sampling in the context of mini-batch selection [32], [55], [148], [226].”
Stochastic variational inference (2012, methodology): “In Gopalan et al. (2012), we applied it to the mixed-membership stochastic blockmodel for uncovering overlapping communities in large social networks.”
Stochastic variational inference for hidden Markov models (2014, methodology): “(15) analogously to [12], generally with a batch factor c varying with each subset y .”
Latent Space Model for Multi-Modal Social Data (2015, methodology): “When the off-diagonal components in B are zero, the only allowed interactions are intra-group ones, which yields strictly assortative Mixed Membership Stochastic Blockmodels (aMMSB) [28].”
Detecting cohesive and 2-mode communities indirected and undirected networks (2014, methodology): “Conceptually CoDA is related to existing work on block models, which are in principle capable of detecting cohesive as well as 2-mode communities [3, 16, 18].”