Recurrent Collective Classification
This work addresses a specific issue in network data classification for researchers and practitioners, offering an incremental improvement over existing methods.
The paper tackles the problem of bias in training iterative collective classifiers for network node labeling, which arises from assuming relational features reflect true labels, by introducing recurrent collective classification (RCC) that directly minimizes loss through gradient-based optimization, resulting in improved accuracy and robustness on real network data.
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.