Learning Modular Structures from Network Data and Node Variables

Provided by: Boston University
Topic: Big Data
Format: PDF
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 dependencies, and unverifiable structural assumptions are often used for regularization. Here, the authors propose an extended model that leverages direct observations about the network in addition to node-specific variables.

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