Latent Multi-group Membership Graph Model by Myunghwan Kim and Jure Leskovec.
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.
Oddly enough, the cited literature in this article cuts off with 1997 and Bearman’s adolescent health survey. I distinctly remember there being network, node, document research prior to 1997.
Not a bad article but I have the feeling I have seen this or something very close to it before. HyTime? With less formalism?
Unless one of my European readers has contributed the solution before I get to the keyboard in the morning (US East Coast), I will take another run at it.
Just so you know, this paragraph, from the introduction, is what caught my eye:
Node features along with the links between them provide rich and complementary sources of information and should be used simultaneously for uncovering, understanding and exploiting the latent structure in the data. In this respect, we develop a new network model considering both the emergence of links of the network and the structure of node features such as user profile information or text of a document.