Collective Matrix Completion via Graph Extraction

Collective matrix completion (CMC) offers a straightforward approach to dealing with data with entries from various sources. Benefiting from the joint structure in the collective matrix, CMC often achieves fast convergence. However, since CMC conducts matrix-level operations, it neglects the entry-wise information that can potentially be very useful for matrix completion. In this paper, to capture the entry-wise information, we propose a method called graph collective matrix completion (GCoMC). Specifically, our method integrates a graph pattern extraction module into CMC via a relational graph convolutional network. Experiments on simulated and real-world datasets show that our method significantly outperforms some existing counterparts.