Latent Multi-group Membership Graph Model
This work addresses network analysis for researchers and practitioners by providing a model for summarizing structure and predicting links and features, but it appears incremental as it builds on existing latent group models.
The paper tackles the problem of modeling networks with rich node features by developing the Latent Multi-group Membership Graph (LMMG) model, which allows nodes to belong to multiple groups for link and feature prediction, and it demonstrates predictive performance on social and document network datasets.
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.