New Attributed Graph Clustering by Bridging Attribute and Topology Spaces

Abstract

We consider the clustering problem of attributed graphs. Our challenge is how we design an effective clustering method that captures the complicated relationship between the topology and the attributes in real-world graphs. We propose NAGC, a new attributed graph clustering method that bridges the attribute space and the topology space. The feature of NAGC is two-hold. 1) NAGC learns a projection function between the topology space and the attribute space so as to capture their complicated relationship, and 2) NAGC leverages the positive unlabeled learning to take the effect of partially observed positive edges into the cluster assignment. We conducted experiments extensively to validate that NAGC performs higher than or comparable to prior arts regarding the clustering quality.