  Attributed 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.
