CommPar: A Community-Based Model Partitioning Approach for Large-Scale Networked Social Dynamics Simulation

Efficient large-scale simulation on multiple processors is essential for social dynamics study but still has been proved to be a challenge. Community structure is a ubiquitous property of social networks. It has significant influence on its dynamics and leads the selection of model partition algorit...

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Vydáno v:2010 IEEE/ACM 14th International Symposium on Distributed Simulation and Real Time Applications s. 7 - 13
Hlavní autoři: Bonan Hou, Yiping Yao
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2010
Témata:
Communities
community detection
Computational modeling
large-scale social simulation
Load modeling
model partitioning
Object oriented modeling
Partitioning algorithms
performance tuning
Program processors
Social network services
ISBN:1424486513, 9781424486519
ISSN:1550-6525
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Shrnutí:Efficient large-scale simulation on multiple processors is essential for social dynamics study but still has been proved to be a challenge. Community structure is a ubiquitous property of social networks. It has significant influence on its dynamics and leads the selection of model partition algorithms a critical performance issue. However, the underlying community structure is not well exploited by existing approaches of load-balancing optimizations, which discounted their effectiveness. This paper proposes COMMPAR, a community-based model partitioning approach, which utilizes the community information of social networks for performance tuning. It contains a two-phased network model partitioning as follows: first, community detection algorithm is employed to discover community structure residing in large-scale social networks, second, those communities are further equally partitioned to achieve an appropriate configuration of simulation execution, and facilitates mapping of the communities onto multiple computer processors. Eventually, the experimental results of a random-walk dynamics simulation show that COMMPAR significantly outperforms several existing partitioning approaches, and can efficiently reduce the overhead of interprocessor communications.
ISBN:1424486513
9781424486519
ISSN:1550-6525
DOI:10.1109/DS-RT.2010.10