# Efficiently Clustering Very Large Attributed Graphs

## TL;DR

This paper introduces SToC, a scalable and flexible clustering algorithm for large attributed graphs that efficiently combines semantic and topological information without needing to predefine the number of clusters.

## Contribution

The paper presents SToC, a novel clustering method that is scalable, robust to different attribute types, and does not require prior knowledge of the number of clusters.

## Key findings

- Efficiently clusters large attributed graphs with high quality.
- Compatible with categorical and quantitative attributes.
- Does not require pre-specifying the number of clusters.

## Abstract

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.