Link-Based Attributed Graph Clustering via Approximate Generative Bayesian Learning

To understand the mechanisms of complex systems, attributed graphs (AGs) are recognized as a valuable model by their capability of describing nontrivial topological structures and rich node contents, and their emergence raises new challenges on the task of graph clustering. Although a variety of computational algorithms have been proposed to perform accurate clustering analysis on AGs, most of them are incapable of inferring the cluster labels of nodes through links, thus falling short of explaining node behaviors on how to formulate overlapping clusters. Moreover, the vast amount of links considerably decreases the computation efficiency if they are explicitly taken into account for AG clustering. To overcome…

• NS
  Natural Science Foundation of Xinjiang Province

  Award: 2023D01E15
• NN
  National Natural Science Foundation of China

  Awards: 62302495, 62373348, 62272078