Corpus ID: 226227414

Fast Network Community Detection with Profile-Pseudo Likelihood Methods.

  title={Fast Network Community Detection with Profile-Pseudo Likelihood Methods.},
  author={Jiangzhou Wang and Jingfei Zhang and Binghui Liu and Ji Zhu and Jingqiu Guo},
  journal={arXiv: Methodology},
The stochastic block model is one of the most studied network models for community detection. It is well-known that most algorithms proposed for fitting the stochastic block model likelihood function cannot scale to large-scale networks. One prominent work that overcomes this computational challenge is Amini et al.(2013), which proposed a fast pseudo-likelihood approach for fitting stochastic block models to large sparse networks. However, this approach does not have convergence guarantee, and… Expand


Pseudo-likelihood methods for community detection in large sparse networks
It is proved that pseudo-likelihood provides consistent estimates of the communities under a mild condition on the starting value, for the case of a block model with two communities. Expand
Consistency of community detection in networks under degree-corrected stochastic block models
It is found that methods based on the degree-corrected stochastic block model are consistent under a wider class of models and that modularity-type methods require parameter constraints for consistency, whereas likelihood-based methods do not. Expand
A Survey on Theoretical Advances of Community Detection in Networks
A survey on the recent theoretical advances of community detection, including graph cut methods, profile likelihoods, the pseudo-likelihood method, the variational method, belief propagation, spectral clustering, and semidefinite relaxations of the stochastic blockmodel. Expand
Community detection with dependent connectivity
  • Y. Yuan, A. Qu
  • Mathematics, Computer Science
  • The Annals of Statistics
  • 2021
This paper proposes a new community detection approach to incorporate within-community dependence of connectivities through the Bahadur representation and shows that incorporating correlation information can lower estimation bias and accelerate algorithm convergence. Expand
Spectral clustering and the high-dimensional stochastic blockmodel
Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social ne tworks, representing people who communicate with each other, areExpand
Using Maximum Entry-Wise Deviation to Test the Goodness of Fit for Stochastic Block Models
Abstract–The stochastic block model is widely used for detecting community structures in network data. How to test the goodness of fit of the model is one of the fundamental problems and has gainedExpand
How Many Communities Are There?
This work proposes composite likelihood BIC (CL-BIC) to select the number of communities, and shows it is robust against possible misspecifications in the underlying stochastic blockmodel assumptions. Expand
Stochastic blockmodels and community structure in networks
  • B. Karrer, M. Newman
  • Mathematics, Computer Science
  • Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2011
This work demonstrates how the generalization of blockmodels to incorporate this missing element leads to an improved objective function for community detection in complex networks and proposes a heuristic algorithm forcommunity detection using this objective function or its non-degree-corrected counterpart. Expand
Community Detection in Networks with Node Features
A new joint community detection criterion is proposed that uses both the network edge information and the node features to detect community structures and performs well on simulated and real networks. Expand
Community detection and stochastic block models: recent developments
  • E. Abbe
  • Mathematics, Computer Science
  • J. Mach. Learn. Res.
  • 2017
The recent developments that establish the fundamental limits for community detection in the stochastic block model are surveyed, both with respect to information-theoretic and computational thresholds, and for various recovery requirements such as exact, partial and weak recovery. Expand