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The use of drug combinations often leads to polypharmacy side effects (POSE).A recent method formulates POSE prediction as a link prediction problem on agraph of drugs and proteins, and solves it with Graph Convolutional Networks(GCNs). However, due to the complex relationships in POSE, this method has highcomputational cost and memory demand. This paper proposes a flexible Tri-graphInformation Propagation (TIP) model that operates on three subgraphs to learnrepresentations progressively by propagation from protein-protein graph todrug-drug graph via protein-drug graph. Experiments show that TIP improvesaccuracy by 7%+, time efficiency by 83$\times$, and space efficiency by3$\times$.

Tri-graph Information Propagation for Polypharmacy Side Effect Prediction