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New Approaches for Performance Optimization and analysis of Large-Scale Dynamic Social Network analysis using Anytime Anywhere algorithms 34

New Approaches for Performance Optimization and Analysis of ...

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34th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Santos, Eunice E. Murugappan, Vairavan Korah, John Univ Illinois Sch Informat Sci Champaign IL 61820 USA Calif State Polytech Univ Pomona Dept Comp Sci Pomona CA 91768 USA

ISBN: (纸本)9781728174457

During the last decade, the availability of large amounts of social network information from various social and socio-technical networks has increased dramatically. These data sources are inherently dynamic with constantly evolving relationships and connections between entities. Research in this area must address the challenge of analyzing these dynamic datasets under potentially strict time constraints. In addition, due to the sheer size of these networks, they tend to be stored and analyzed on distributed platforms. In our previous work, we designed methodologies which are anytime and anywhere to design scalable parallel/distributed algorithms that incorporate different forms of network changes. In this work, we will investigate various schemas to balance the incorporation of dynamic network changes that will substantially reduce idleness and load imbalances among processors. We will show theoretically that in most cases our buffer-based methodology performs better than the more common way of handling changes as they come in.

关键词： Anytime Anywhere algorithms Buffer-based methods graph algorithms and analysis Large-scale dynamic social network analysis Parallel/Distributed algorithms Performance analysis

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Memory Efficient Edge Addition Designs for Large and Dynamic Social Networks

Memory Efficient Edge Addition Designs for Large and Dynamic...

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35th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Santos, Eunice E. Murugappan, Vairavan Korah, John Univ Illinois Sch Informat Sci Champaign IL 61820 USA Calif State Polytech Univ Pomona Dept Comp Sci Pomona CA 91768 USA

ISBN: (纸本)9781665435772

The availability of large volumes of social network data from a variety of social and socio-technical networks has greatly increased. These networks provide critical insights into understanding various domains including business, healthcare, and disaster management. The relationships and interactions between different entities represented in most of these data sources are constantly evolving. graph processing and analysis methodologies that can effectively integrate data changes while minimizing recomputations are needed to handle these dynamic networks. In addition, the size of these information sources is constantly increasing, therefore we need designs that can perform analysis that are memory efficient in order to address resource constraints. In this paper, we show how our anytime anywhere framework can be used to construct memory-efficient closeness centrality algorithms. In particular, we will show how dynamic edge additions can be efficiently handled in the proposed scheme.

关键词： Anytime anywhere algorithms Memory efficient graph algorithms graph algorithms and analysis Large-scale dynamic social network analysis Parallel/Distribated algorithms

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