Measuring Proximity in Attributed Networks for Community Detection
This work addresses community detection for network analysts by incorporating node attributes, but it is incremental as it extends existing methods rather than introducing a new paradigm.
The authors tackled the problem of community detection in attributed networks by extending existing graph proximity measures to incorporate node attributes, and they applied these measures with spectral clustering to real-world networks.
Proximity measures on graphs have a variety of applications in network analysis, including community detection. Previously they have been mainly studied in the context of networks without attributes. If node attributes are taken into account, however, this can provide more insight into the network structure. In this paper, we extend the definition of some well-studied proximity measures to attributed networks. To account for attributes, several attribute similarity measures are used. Finally, the obtained proximity measures are applied to detect the community structure in some real-world networks using the spectral clustering algorithm.