Neural Networks for Relational Data

Abstract

While deep networks have been enormously successful, they rely on flat-feature vector representations. Using them in structured domains requires significant feature engineering. Such domains rely on relational representations to capture complex relationships between entities and their attributes. Thus, we consider the problem of learning neural networks for relational data. We distinguish ourselves from current approaches that rely on expert hand-coded rules by learning higher-order random-walk features to capture local structural interactions and the resulting network architecture. We further exploit parameter tying, where instances of the same rule share parameters. Our experimental results demonstrate the effectiveness of the proposed approach over multiple neural net baselines as well as state-of-the-art relational models.