  Molecular interaction networks are powerful resources for the discovery. They
are increasingly used with machine learning methods to predict biologically
meaningful interactions. While deep learning on graphs has dramatically
advanced the prediction prowess, current graph neural network (GNN) methods are
optimized for prediction on the basis of direct similarity between interacting
nodes. In biological networks, however, similarity between nodes that do not
directly interact has proved incredibly useful in the last decade across a
variety of interaction networks. Here, we present SkipGNN, a graph neural
network approach for the prediction of molecular interactions. SkipGNN predicts
molecular interactions by not only aggregating information from direct
interactions but also from second-order interactions, which we call skip
similarity. In contrast to existing GNNs, SkipGNN receives neural messages from
two-hop neighbors as well as immediate neighbors in the interaction network and
non-linearly transforms the messages to obtain useful information for
prediction. To inject skip similarity into a GNN, we construct a modified
version of the original network, called the skip graph. We then develop an
iterative fusion scheme that optimizes a GNN using both the skip graph and the
original graph. Experiments on four interaction networks, including drug-drug,
drug-target, protein-protein, and gene-disease interactions, show that SkipGNN
achieves superior and robust performance, outperforming existing methods by up
to 28.8\% of area under the precision recall curve (PR-AUC). Furthermore, we
show that unlike popular GNNs, SkipGNN learns biologically meaningful
embeddings and performs especially well on noisy, incomplete interaction
networks.
