Learning Networked Exponential Families with Network Lasso

TL;DR

This paper introduces networked exponential families and a scalable learning method called network Lasso, which efficiently models and clusters networked data by leveraging both topology and features.

Contribution

It presents a novel probabilistic model for heterogeneous networked data and an efficient primal-dual splitting algorithm for scalable learning.

Findings

Effective clustering of networked data points

Scalable message passing implementation

Flexible modeling of heterogeneous datasets

Abstract

We propose networked exponential families to jointly leverage the information in the topology as well as the attributes (features) of networked data points. Networked exponential families are a flexible probabilistic model for heterogeneous datasets with intrinsic network structure. These models can be learnt efficiently using network Lasso which implicitly pools or clusters the data points according to the intrinsic network structure and the local likelihood. The resulting method can be formulated as a non-smooth convex optimization problem which we solve using a primal-dual splitting method. This primal-dual method is appealing for big data applications as it can be implemented as a highly scalable message passing algorithm.