K-Core Decomposition of Large Networks on a Single PC.

Studying the topology of a network is critical to inferring underlying dynamics such as tolerance to failure, group behavior and spreading patterns. k-core decomposition is a well-established metric which partitions a graph into layers from external to more central vertices. In this paper we aim to explore whether k-core decomposition of large networks can be computed using a consumer-grade PC. We feature implementations of the \"vertex-centric\" distributed protocol introduced by Montresor, De Pellegrini and Miorandi on GraphChi and Webgraph. Also, we present an accurate implementation of the Batagelj and Zaversnik algorithm for k-core decomposition in Webgraph. With our implementations, we show that we can efficiently handle networks of billions of edges using a single consumer-level machine within reasonable time and can produce excellent approximations in only a fraction of the execution time. To the best of our knowledge, our biggest graphs are considerably larger than the graphs considered in the literature. Next, we present an optimized implementation of an external-memory algorithm (EMcore) by Cheng, Ke, Chu, and Özsu. We show that this algorithm also performs well for large datasets, however, it cannot predict whether a given memory budget is sufficient for a new dataset. We present a thorough analysis of all algorithms concluding that it is viable to compute k-core decomposition for large networks in a consumer-grade PC.