  We propose a new method for training iterative collective classifiers for
labeling nodes in network data. The iterative classification algorithm (ICA) is
a canonical method for incorporating relational information into
classification. Yet, existing methods for training ICA models rely on the
assumption that relational features reflect the true labels of the nodes. This
unrealistic assumption introduces a bias that is inconsistent with the actual
prediction algorithm. In this paper, we introduce recurrent collective
classification (RCC), a variant of ICA analogous to recurrent neural network
prediction. RCC accommodates any differentiable local classifier and relational
feature functions. We provide gradient-based strategies for optimizing over
model parameters to more directly minimize the loss function. In our
experiments, this direct loss minimization translates to improved accuracy and
robustness on real network data. We demonstrate the robustness of RCC in
settings where local classification is very noisy, settings that are
particularly challenging for ICA.
