CoreCluster: A Degeneracy Based Graph Clustering Framework

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 tools 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 a 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 a 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. Introduction Detecting clusters or communities in graphs constitutes a cornerstone problem with many applications in several disciplines. Characteristic application domains include social and information network analysis, biological networks, recommender systems and image segmentation. Due to its importance and multidisciplinary nature, the problem of graph clustering has received great attention from the research community and numerous algorithms have been proposed (see (Fortunato 2010) for a survey in the area). Spectral clustering methods (e.g., (Ng, Jordan, and Weiss 2001)) impose a high cost of computing resources both in time and space regardless of the data on which it is going to be applied (Fortunato 2010). Other well-known approaches for community detection are the ones based on modularity optimization (Newman and Girvan 2004; Clauset, Newman, and Moore 2004), stochastic flow simulation (Satuluri and Parthasarathy 2009) and local partitioning methods (Fortunato 2010). In any case, scalability is still a major challenge in the graph clustering task, especially nowadays with the significant increase of the graphs’ size. Typically, there are two main methodologies for scaling up a graph clustering method: (i) algorithm-oriented Copyright c 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. and (ii) data-oriented. The first one considers the algorithm of interest and appropriately optimizes – whenever is possible – the “parts” of the algorithm responsible for scalability issues. Prominent examples here are the fast modularity optimization method (Clauset, Newman, and Moore 2004) and the scalable flow-based Markov clustering algorithm (Satuluri and Parthasarathy 2009). The second and widely used methodology is to rely on sampling/sparsification techniques. In this case, the size of the graph onto which the algorithm will operate is reduced, by disregarding nodes/edges. However, in this approach possibly useful structural information of the graph (i.e., nodes/edges) is ignored. In this paper, we propose CORECLUSTER, a graph clustering framework that capitalizes on the notion of graph degeneracy – also known as k-core decomposition (Seidman 1983). The main idea behind our approach is to combine any known graph clustering algorithm with an easy-tocompute, clustering-preserving hierarchical representation of the graph – as produced by the k-core decomposition – towards a scalable graph clustering tool. The k-core of a graph is a maximal size subgraph where each node has at least k neighbors in the subgraph (we say that k is the rank of such a core). The maximum k for which a graph contains a k-core is known as its degeneracy. We refer to this core as “the densest core”. Intuitively, the k-core of such a graph is located in its “densest territories”. Based on this idea, we show that the densest cores of a graph are roughly maintaining its clustering structure and thus constitute good starting points (seed subgraphs) for computing it. Given the fact that the size of the densest core of a graph is orders of magnitude smaller than that of the original graph, we apply a clustering algorithm starting from its densest core and then, on the resulting structure, we incrementally cluster the rest of the nodes in the lower rank cores in decreasing order – following the hierarchy produced by the k-core decomposition. The main contributions of this paper are the following: • Clustering Framework: We introduce CORECLUSTER, a scalable degeneracy-based graph clustering framework, that can be used along with any known graph clustering algorithm. We show how CORECLUSTER utilizes the kcore decomposition of a graph in order to (i) select seed subgraphs for starting the clustering process and (ii) expand the already formed clusters or create new ones. • Scalability and Accuracy Analysis: We discuss analytically the ability of CORECLUSTER to scale-up, describing its expected running time. We also justify why the k-core structure captures the clustering properties of a graph, thus being able to indicate good seed subgraphs for a clustering algorithm. • Experiments: We perform an extensive experimental evaluation regarding the efficiency and accuracy of CORECLUSTER, both on synthetic and real-world graphs. The experimental results show that the time complexity is improved by 3-4 orders of magnitude (compared to a baseline algorithm), especially for large graphs. Moreover, for graphs with inherent community structure, the quality of the results is maintained or even is improved.