01602nas a2200133   4500008004100000245005600041210005600097520113100153100002301284700001901307700002501326700002401351856009301375       2017                        eng d00aEfficiently Clustering Very Large Attributed Graphs0 aEfficiently Clustering Very Large Attributed Graphs3 aAttributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.1 aBaroni, Alessandro1 aConte, Alessio1 aPatrignani, Maurizio1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/efficiently-clustering-very-large-attributed-graphs