  As a result of the recent popularity of social networks and the increase in
the number of research papers published across all fields, attributed networks
consisting of relationships between objects, such as humans and the papers,
that have attributes are becoming increasingly large. Therefore, various
studies for clustering attributed networks into sub-networks are being actively
conducted. When clustering attributed networks using spectral clustering, the
clustering accuracy is strongly affected by the quality of the similarity
matrices, which are input into spectral clustering and represent the
similarities between pairs of objects. In this paper, we aim to increase the
accuracy by refining the matrices before applying spectral clustering to them.
We verify the practicability of our proposed method by comparing the accuracy
of spectral clustering with similarity matrices before and after refining them.
