In this paper, we will examine the problem of clustering massive graph streams. Graph clustering poses significant challenges because of the complex structures which may be present in the underlying data. The massive size of the underlying graph makes explicit structural enumeration very difficult. Consequently, most techniques for clustering multi-dimensional data are difficult to generalize to the case of massive graphs. Recently, methods have been proposed for clustering graph data, though these methods are designed for static data, and are not applicable to the case of graph streams. Furthermore, these techniques are especially not effective for the case of massive graphs, since a huge number of distinct edges may need to be tracked simultaneously. This results in storage and computational challenges during the clustering process. In order to deal with the natural problems arising from the use of massive disk-resident graphs, we will propose a technique for creating hash-compressed micro-clusters from graph streams. The compressed micro-clusters are designed by using a hash-based compression of the edges onto a smaller domain space. We will provide theoretical results which show that the hash-based compression continues to maintain bounded accuracy in terms of distance computations. We will provide experimental results which illustrate the accuracy and efficiency of the underlying method.
REF proposed a method to cluster massive graph streams by extending micro-clusters.
On clustering graph streams
in SIAM Conf. on Data Mining
Computer Science