Date of Award
12-2016
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Jennifer Neville
Committee Chair
Jennifer Neville
Committee Member 1
Dan Goldwasser
Committee Member 2
Bruno Ribeiro
Committee Member 3
Mark D. Ward
Abstract
Collective inference is widely used to improve classification in network datasets. However, despite recent advances in deep learning and the successes of recurrent neural networks (RNNs), researchers have only just recently begun to study how to apply RNNs to heterogeneous graph and network datasets. There has been recent work on using RNNs for unsupervised learning in networks (e.g., graph clustering, node embedding) and for prediction (e.g., link prediction, graph classification), but there has been little work on using RNNs for node-based relational classification tasks. In this paper, we provide an end-to-end learning framework using RNNs for collective inference. Our main insight is to transform a node and its set of neighbors into an unordered sequence (of varying length) and use an LSTM-based RNN to predict the class label as the output of that sequence. We develop a collective inference method, which we refer to as Deep Collective Inference (DCI), that uses semi-supervised learning in partially-labeled networks and two label distribution correction mechanisms for imbalanced classes. We compare to several alternative methods on seven network datasets. DCI achieves up to a 12% reduction in error compared to the best alternative and a 25% reduction in error on average over all methods, for all label proportions.
Recommended Citation
Moore, John A., "Deep collective inference" (2016). Open Access Theses. 879.
https://docs.lib.purdue.edu/open_access_theses/879