Overcoming uncertainty for within network relational machine learning
Pfei|er, Joseph J. III Ph.D. Purdue University, May 2015. Overcoming Uncertainty for Within Network Relational Machine Learning. Major Professor: Jennifer Neville. People increasingly communicate through email and social networks to maintain friendships and conduct business, as well as share online content such as pictures, videos and products. Relational machine learning (RML) utilizes a set of observed attributes and network structure to predict corresponding labels for items; for example, to predict individuals engaged in securities fraud, we can utilize phone calls and workplace information to make joint predictions over the individuals. However, in large scale and partially observed network domains, missing labels and edges can significantly impact standard relational machine learning methods by introducing bias into the learning and inference processes. In this dissertation, we identify the e|ects on parameter estimation, correct the biases, and model the uncertainty of the missing data to improve predictive performance. In particular, we investigate this issue on a variety of modeling scenarios and prediction problems. First, we introduce the Transitive Chung Lu (TCL) random graph model for modeling the conditional distribution of edges given a partially observed network. This model fits within a class of scalable generative graph models with scalable sampling processes that we generalize to model distributions of networks with correlated attribute variables via Attributed Graph Models. Second, we utilize TCL to incorporate edge probabilities into relational learning and inference models for partially observed network domains. As part of this work, we give a linear time algorithm to perform variational inference over a squared network. We apply the resulting semi supervised model, Probabilistic Relational EM (PR EM) to the Active Exploration domain to