David A. Bader
IEEE Fellow
AAAS Fellow
College of Computing
Georgia Tech
Atlanta, GA 30332



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 socio-economic 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 nearly 28 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.

Publication History

Versions of this paper appeared as:
  1. K. Madduri and D.A. Bader, ``Compact Graph Representations and Parallel Connectivity Algorithms for Massive Dynamic Network Analysis,'' 23rd IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rome, Italy, May 25-29, 2009.

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Last updated: October 6, 2009


Computational Biology

Parallel Computing