论文标题
精确地将相似性矩阵准确地归因于集群网络
Refining Similarity Matrices to Cluster Attributed Networks Accurately
论文作者
论文摘要
由于社交网络的最新流行以及在所有领域发表的研究论文数量的增加，归因于由人类和论文等对象之间关系的网络越来越大。因此，正在积极进行各种将归因于子网络归因于子网络的研究。当群集使用光谱群集归因于网络时，聚类的精度会受到相似性矩阵质量的强烈影响，这些矩阵的质量被输入到光谱群集中，并表示对象对之间的相似性。在本文中，我们旨在通过在对它们应用光谱聚类之前提高矩阵来提高准确性。我们通过比较频谱聚类在完善之前和之后的相似性矩阵的准确性来验证我们提出的方法的实用性。
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.
