  The use of drug combinations often leads to polypharmacy side effects (POSE).
A recent method formulates POSE prediction as a link prediction problem on a
graph of drugs and proteins, and solves it with Graph Convolutional Networks
(GCNs). However, due to the complex relationships in POSE, this method has high
computational cost and memory demand. This paper proposes a flexible Tri-graph
Information Propagation (TIP) model that operates on three subgraphs to learn
representations progressively by propagation from protein-protein graph to
drug-drug graph via protein-drug graph. Experiments show that TIP improves
accuracy by 7%+, time efficiency by 83$\times$, and space efficiency by
3$\times$.
