Graph-assisted Matrix Completion in a Multi-clustered Graph Model

We consider a matrix completion problem that exploits social graph as side information. We develop a computationally efficient algorithm that achieves the optimal sample complexity for the entire regime of graph information under the multiple cluster setting (to be detailed). The key idea is to incorporate a switching mechanism which selects the information employed in the first clustering step, between the following two types: graph & matrix ratings. Our experimental results on both synthetic and real data corroborate our theoretical result as well as demonstrate that our algorithm outperforms prior algorithms that leverage graph side information.