When a data source contains relationship information that can be irregular and complex, representing it as a graph consisting of nodes and edges is an inevitable choice. With the rapid growth in data availability, it becomes more important to utilize multiple data sources containing different but complementary information for a given task. Using multiple graphs can technically be interpreted as finding the optimal combination of each graph. There have been various approaches for graph integration or graph fusion, but most of them have suffered from scalability issues due to long computation times. To overcome this difficulty, we propose a novel graph integration method that can be performed quickly and simply even for large-sized networks by using the approximation technique of Neumann expansion in the process of maximum likelihood estimation. As a result of various experiments conducted on several datasets, the node classification performance of the proposed method was competitive compared to existing methods, and the computation speed was extremely faster. In particular, the proposed method showed very fast speed even when the number of nodes in the network increased, which proves that the proposed method has very high scalability.
