New Approaches for Performance Optimization and Analysis of Large-Scale Dynamic Social Network Analysis using Anytime Anywhere Algorithms

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 are...

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Vydané v:2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) s. 1123 - 1128
Hlavní autori: Santos, Eunice E., Murugappan, Vairavan, Korah, John
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.05.2020
Predmet:
Anytime Anywhere algorithms
Buffer-based methods
Graph algorithms and analysis
Heuristic algorithms
Large-scale dynamic social network analysis
Measurement
Optimization
Parallel/Distributed algorithms
Performance analysis
Processor scheduling
Program processors
Runtime
Social network services
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Popis
Shrnutí: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.
DOI:10.1109/IPDPSW50202.2020.00186