Efficient discovery of overlapping communities in massive networks

By Prem Gopalan, David M. Blei. 2013

305 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

How researchers see this paper

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

In Experimental and Cognitive Psychology

1. Jake M. Hofman
2. Emmanuel Abbe
3. David M. Blei
4. Prem Gopalan
5. Martin Rosval
6. Ludvig Bohlin
7. Andrea Lancichinetti
8. Daniel Edler

In Statistical and Nonlinear Physics

1. Prem Gopalan
2. David M. Blei
3. Xiao Wang
4. Peng Cui
5. Jake M. Hofman
6. Wenwu Zhu
7. Jian Pei
8. Emmanuel Abbe

Citations in the corpus

• Scalable Recommendation with Poisson Factorization (2013, methodology): “Independently of GaP and user behavior models, Poisson factorization has been studied in the context of signal processing for source separation [5, 12] and for the purpose of detecting community structure in network data [1, 11].”
• Community Detection in General Stochastic Block models: Fundamental Limits and Efficient Algorithms for Recovery (2015, background): “The SBM recently came back at the center of the attention at both the practical level, due to extensions allowing overlapping communities [33] that have proved to fit well real data sets in massive networks [34], and at the theoretical level due to new phase transition phenomena discovered for the t”
• Scalable Recommendation with Hierarchical Poisson Factorization (2015, methodology): “Independently of GaP and user behavior models, Poisson factorization has been studied in the context of signal processing for source separation [5, 15] and for detecting community structure in network data [1, 13].”
• Measuring Scholarly Impact (2014, methodology): “Block models Methods based on block models identify blocks of nodes (Gopalan & Blei, 2013; Karrer & Newman, 2011; Peixoto, 2013) with common properties.”
• Exact Recovery in the Stochastic Block Model (2014, background): “…specifically, the stochastic block model has been at the center of the attention in a large body of literature [22, 38, 18, 37, 33, 15, 24, 28, 14, 13, 27, 30, 31], as a testbed for algorithms (see [9] for a survey) as well as a scalable model for large data sets (see [21] and reference therein).”
• Community Preserving Network Embedding (2017, methodology): “By fitting an empirical network to this model, the underlying community structure can be inferred (Karrer and Newman 2011; Gopalan and Blei 2013; Jin et al. 2016).”