  While deep networks have been enormously successful over the last decade,
they rely on flat-feature vector representations, which makes them unsuitable
for richly structured domains such as those arising in applications like social
network analysis. 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 relational random-walk-based features to capture local structural
interactions and the resulting network architecture. We further exploit
parameter tying of the network weights of the resulting relational neural
network, where instances of the same type share parameters. Our experimental
results across several standard relational data sets demonstrate the
effectiveness of the proposed approach over multiple neural net baselines as
well as state-of-the-art statistical relational models.
