  Multi-scale biomedical knowledge networks are expanding with emerging
experimental technologies that generates multi-scale biomedical big data. Link
prediction is increasingly used especially in bipartite biomedical networks to
identify hidden biological interactions and relationshipts between key entities
such as compounds, targets, gene and diseases. We propose a Graph Neural
Networks (GNN) method, namely Graph Pair based Link Prediction model (GPLP),
for predicting biomedical network links simply based on their topological
interaction information. In GPLP, 1-hop subgraphs extracted from known network
interaction matrix is learnt to predict missing links. To evaluate our method,
three heterogeneous biomedical networks were used, i.e. Drug-Target Interaction
network (DTI), Compound-Protein Interaction network (CPI) from NIH Tox21, and
Compound-Virus Inhibition network (CVI). Our proposed GPLP method significantly
outperforms over the state-of-the-art baselines. In addition, different network
incompleteness is analysed with our devised protocol, and we also design an
effective approach to improve the model robustness towards incomplete networks.
Our method demonstrates the potential applications in other biomedical
networks.
