  In recent years, substantial progress has been made on Graph Convolutional
Networks (GCNs). However, the computing of GCN usually requires a large memory
space for keeping the entire graph. In consequence, GCN is not flexible enough,
especially for large scale graphs in complex real-world applications.
Fortunately, methods based on Matrix Factorization (MF) naturally support
constructing mini-batches, and thus are more friendly to distributed computing
compared with GCN. Accordingly, in this paper, we analyze the connections
between GCN and MF, and simplify GCN as matrix factorization with unitization
and co-training. Furthermore, under the guidance of our analysis, we propose an
alternative model to GCN named Unitized and Co-training Matrix Factorization
(UCMF). Extensive experiments have been conducted on several real-world
datasets. On the task of semi-supervised node classification, the experimental
results illustrate that UCMF achieves similar or superior performances compared
with GCN. Meanwhile, distributed UCMF significantly outperforms distributed GCN
methods, which shows that UCMF can greatly benefit large scale and complex
real-world applications. Moreover, we have also conducted experiments on a
typical task of graph embedding, i.e., community detection, and the proposed
UCMF model outperforms several representative graph embedding models.
