Compact Graph Representations and Parallel Connectivity Algorithms for Massive Dynamic Network Analysis
Graph-theoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socioeconomic interactions, social networking web sites, communication traffic, and scientific computing can be intuitively modeled as graphs. We present the first study of novel high-performance combinatorial techniques for analyzing large-scale information networks, encapsulating dynamic interaction data in the order of billions of entities. We present new data structures to represent dynamic interaction networks, and discuss algorithms for processing parallel insertions and deletions of edges in small-world networks. With these new approaches, we achieve an average performance rate of 25 million structural updates per second and a parallel speedup of nearly28 on a 64-way Sun UltraSPARC T2 multicore processor, for insertions and deletions to a small-world network of 33.5 million vertices and 268 million edges. We also design parallel implementations of fundamental dynamic graph kernels related to connectivity and centrality queries. Our implementations are freely distributed as part of the open-source SNAP (Small-world Network Analysis and Partitioning) complex network analysis framework.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- Computational Research Division
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 951103
- Report Number(s):
- LBNL-1704E; TRN: US200911%%332
- Resource Relation:
- Conference: 23rd IEEE International Parallel and Distributed Processing Symposium, Rome, Italy, May 25-29, 2009
- Country of Publication:
- United States
- Language:
- English
Similar Records
A Faster Parallel Algorithm and Efficient Multithreaded Implementations for Evaluating Betweenness Centrality on Massive Datasets
ConnectIt: a framework for static and incremental parallel graph connectivity algorithms