# Learning Modular Structures from Network Data and Node Variables

@inproceedings{Azizi2014LearningMS, title={Learning Modular Structures from Network Data and Node Variables}, author={Elham Azizi and Edoardo M. Airoldi and James E. Galagan}, booktitle={ICML}, year={2014} }

A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, groups are termed modules, and the focus is on estimating the network structure among modules. However, estimation solely from node-specific variables can lead to spurious…

## 12 Citations

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## References

SHOWING 1-10 OF 47 REFERENCES

### Joint Learning of Modular Structures from Multiple Data Types

- Computer Science
- 2013

An extended model inspired by module networks and stochastic blockmodels for joint learning of structures from observed object variables and relational data among objects and a reversible-jump MCMC learning procedure for learning modules and model parameters is proposed.

### Learning Module Networks

- Computer ScienceJ. Mach. Learn. Res.
- 2005

Evaluation on real data in the domains of gene expression and the stock market shows that module networks generalize better than Bayesian networks, and that the learned module network structure reveals regularities that are obscured in learnedBayesian networks.

### Validating module network learning algorithms using simulated data

- Computer ScienceBMC Bioinformatics
- 2007

The use of the synthetic data generator SynTReN to develop and test an alternative module network learning strategy, which is incorporated in the software package LeMoNe, and evidence is provided that this alternative strategy has several advantages with respect to existing methods is provided.

### Module networks revisited: computational assessment and prioritization of model predictions

- Computer ScienceBioinform.
- 2009

This work revisits the approach of Segal et al. to infer regulatory modules and their condition-specific regulators from gene expression data and uses a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data.

### Sparse matrix-variate Gaussian process blockmodels for network modeling

- Computer ScienceUAI
- 2011

This work proposes a new relational model for network data, Sparse Matrix-variate Gaussian process Blockmodel (SMGB), which generalizes popular bilinear generative models and captures nonlinear network interactions using a matrix-variations process with latent membership variables.

### Overlapping stochastic block models with application to the French political blogosphere

- Computer Science
- 2011

This approach allows the vertices to belong to multiple clusters, and, to some extent, generalizes the well-known Stochastic Block Model, and proposes an approximate inference procedure, based on global and local variational techniques.

### Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge

- Computer ScienceStatistical applications in genetics and molecular biology
- 2007

These findings quantify to what extent the inclusion of independent prior knowledge improves the network reconstruction accuracy, and the values of the hyperparameters inferred with the proposed scheme were found to be close to optimal with respect to minimizing the reconstruction error.

### Mixed Membership Stochastic Blockmodels

- Computer ScienceNIPS
- 2008

This paper describes a latent variable model of such data called the mixed membership stochastic blockmodel, which extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation.

### Stochastic Blockmodels for Directed Graphs

- Computer Science
- 1987

An iterative scaling algorithm is presented for fitting the model parameters by maximum likelihood and blockmodels that are simple extensions of the p 1 model are proposed specifically for such data.

### Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure

- Mathematics, Computer Science
- 1997

A posteriori blockmodeling for graphs is proposed and it is shown that when the number of vertices tends to infinity while the probabilities remain constant, the block structure can be recovered correctly with probability tending to 1.