  Graph clustering or community detection constitutes an important task for
investigating the internal structure of graphs, with a plethora of applications
in several domains. Traditional techniques for graph clustering, such as
spectral methods, typically suffer from high time and space complexity. In this
article, we present CoreCluster, an efficient graph clustering framework based
on the concept of graph degeneracy, that can be used along with any known graph
clustering algorithm. Our approach capitalizes on processing the graph in an
hierarchical manner provided by its core expansion sequence, an ordered
partition of the graph into different levels according to the k-core
decomposition. Such a partition provides an efficient way to process the graph
in an incremental manner that preserves its clustering structure, while making
the execution of the chosen clustering algorithm much faster due to the smaller
size of the graph's partitions onto which the algorithm operates. An
experimental analysis on a multitude of real and synthetic data demonstrates
that our approach can be applied to any clustering algorithm accelerating the
clustering process, while the quality of the clustering structure is preserved
or even improved.
