SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale

We present SNAP (Small-world Network Analysis and Partitioning), an open-source graph framework for exploratory study and partitioning of large-scale networks. To illustrate the capability of SNAP, we discuss the design, implementation, and performance of three novel parallel community detection algorithms that optimize modularity, a popular measure for clustering quality in social network analysis. In order to achieve scalable parallel performance, we exploit typical network characteristics of small-world networks, such as the low graph diameter, sparse connectivity, and skewed degree distribution. We conduct an extensive experimental study on real-world graph instances and demonstrate that our parallel schemes, coupled with aggressive algorithm engineering for smallworld networks, give significant running time improvements over existing modularity-based clustering heuristics, with little or no loss in clustering quality. For instance, our divisive clustering approach based on appro...

• Type: Conference
• Year: 2008
• Where: IPPS
• Authors: David A. Bader, Kamesh Madduri
• Keywords: Distributed And Parallel Computing, Graph Instances, IPPS 2008, Small-world Networks, Smallworld Networks