Collective inference for network data with copula latent markov networks

The popularity of online social networks and social media has increased the amount of linked data available in Web domains. Relational and Gaussian Markov networks have both been applied successfully for classification in these relational settings. However, since Gaussian Markov networks model joint distributions over continuous label space, it is difficult to use them to reason about uncertainty in discrete labels. On the other hand, relational Markov networks model probability distributions over discrete label space, but since they condition on the graph structure, the marginal probability for an instance will vary based on the structure of the subnetwork observed around the instance. This implies that the marginals will not be identical across instances and can sometimes result in poor prediction performance. In this work, we propose a novel latent relational model based on copulas which allows use to make predictions in a discrete label space while ensuring identical marginals and at the same time incorporating some desirable properties of modeling relational dependencies in a continuous space. While copulas have recently been used for descriptive modeling, they have not been used for collective classification in large scale network data and the associated conditional inference problem has not been considered before. We develop an approximate inference algorithm, and demonstrate empirically that our proposed Copula Latent Markov Network models based on approximate inference outperform a number of competing relational classification models over a range of real-world relational classification tasks.