A model-based approach to attributed graph clustering

341 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure (2018, background): “BAGC/GBAGC (Xu et al. 2012; 2014) learns a posterior distribution over the model parameters; however, it does not account for the frequently observed power law distribution of node degrees.”
Community detection in Attributed Network (2018, methodology): “[30] developed a Bayesian probabilistic model for attributed graphs denoted BAGC, and then formulate the clustering problem as standard probabilistic inference problem to find the clustering that gives the highest probability.”
Community Detection in Networks with Node Attributes (2013, methodology): “Approaches based on topic models [4], [27], [38], [40] allow overlapping communities to be detected.”
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning (2021, methodology): “The algorithm for clustering attribute communities is shown in Appendix C [72].”
Effective Community Search for Large Attributed Graphs (2016, background): “Non-attributed [22, 11] [26, 5, 4, 15, 18] Attributed [34, 21, 20, 29, 23] ACQ (This paper)”