  Drug-side effect prediction has become an essential area of research in the
field of pharmacology. As the use of medications continues to rise, so does the
importance of understanding and mitigating the potential risks associated with
them. At present, researchers have turned to data-driven methods to predict
drug-side effects. Drug-side effect prediction is a link prediction problem,
and the related data can be described from various perspectives. To process
these kinds of data, a multi-view method, called Multiple Kronecker RLS
fusion-based link propagation (MKronRLSF-LP), is proposed. MKronRLSF-LP extends
the Kron-RLS by finding the consensus partitions and multiple graph Laplacian
constraints in the multi-view setting. Both of these multi-view settings
contribute to a higher quality result. Extensive experiments have been
conducted on drug-side effect datasets, and our empirical results provide
evidence that our approach is effective and robust.
