Learning Networked Exponential Families with Network Lasso
This work addresses the problem of leveraging network structure in probabilistic modeling for researchers and practitioners dealing with networked data, though it appears incremental as it builds on existing methods like network Lasso.
The paper tackles modeling heterogeneous datasets with network structure by proposing networked exponential families, which are learned efficiently using network Lasso to pool data points based on network topology and local likelihood, resulting in a scalable message-passing algorithm for big data applications.
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.