### Abstract

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 highperformance combinatorial techniques for analyzing largescale 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.

Original language | English (US) |
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Title of host publication | IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium |

DOIs | |

State | Published - Nov 25 2009 |

Event | 23rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2009 - Rome, Italy Duration: May 23 2009 → May 29 2009 |

### Publication series

Name | IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium |
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### Other

Other | 23rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2009 |
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Country | Italy |

City | Rome |

Period | 5/23/09 → 5/29/09 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computational Theory and Mathematics
- Hardware and Architecture
- Software

### Cite this

*IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium*[5161060] (IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium). https://doi.org/10.1109/IPDPS.2009.5161060

}

*IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium.*, 5161060, IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium, 23rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2009, Rome, Italy, 5/23/09. https://doi.org/10.1109/IPDPS.2009.5161060

**Compact graph representations and parallel connectivity algorithms for massive dynamic network analysis.** / Madduri, Kamesh; Bader, David A.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Compact graph representations and parallel connectivity algorithms for massive dynamic network analysis

AU - Madduri, Kamesh

AU - Bader, David A.

PY - 2009/11/25

Y1 - 2009/11/25

N2 - 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 highperformance combinatorial techniques for analyzing largescale 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.

AB - 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 highperformance combinatorial techniques for analyzing largescale 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.

UR - http://www.scopus.com/inward/record.url?scp=70449844302&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70449844302&partnerID=8YFLogxK

U2 - 10.1109/IPDPS.2009.5161060

DO - 10.1109/IPDPS.2009.5161060

M3 - Conference contribution

AN - SCOPUS:70449844302

SN - 9781424437504

T3 - IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium

BT - IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium

ER -