Learning graphical models with hubs

Kean Ming Tan, Palma London, Karthik Mohan, Su In Lee, Maryam Fazel, Daniela Witten

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


We consider the problem of learning a high-dimensional graphical model in which there are a few hub nodes that are densely-connected to many other nodes. Many authors have studied the use of an ℓ1 penalty in order to learn a sparse graph in the high-dimensional setting. However, the ℓ1 penalty implicitly assumes that each edge is equally likely and independent of all other edges. We propose a general framework to accommodate more realistic networks with hub nodes, using a convex formulation that involves a row-column overlap norm penalty. We apply this general framework to three widely-used probabilistic graphical models: the Gaussian graphical model, the covariance graph model, and the binary Ising model. An alternating direction method of multipliers algorithm is used to solve the corresponding convex optimization problems. On synthetic data, we demonstrate that our proposed framework outperforms competitors that do not explicitly model hub nodes. We illustrate our proposal on a webpage data set and a gene expression data set.

Original languageEnglish (US)
Article numberA22
Pages (from-to)3297-3331
Number of pages35
JournalJournal of Machine Learning Research
StatePublished - Jan 1 2015

Bibliographical note

Publisher Copyright:
© 2014 Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, and Daniela Witten.


  • Alternating direction method of multipliers
  • Binary network
  • Covariance graph
  • Gaussian graphical model
  • Hub
  • Lasso


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