Latent Multi-group Membership Graph Model

TL;DR

The paper introduces the Latent Multi-group Membership Graph (LMMG) model, which captures complex network structures by modeling nodes with multiple group memberships, enabling link prediction and feature inference.

Contribution

It presents a novel probabilistic model that integrates node features and multiple group memberships, along with efficient algorithms for inference and learning.

Findings

Effective in summarizing network structure

Accurate link prediction on social and document networks

Successful feature missing data imputation

Abstract

We develop the Latent Multi-group Membership Graph (LMMG) model, a model of networks with rich node feature structure. In the LMMG model, each node belongs to multiple groups and each latent group models the occurrence of links as well as the node feature structure. The LMMG can be used to summarize the network structure, to predict links between the nodes, and to predict missing features of a node. We derive efficient inference and learning algorithms and evaluate the predictive performance of the LMMG on several social and document network datasets.