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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) s. 1123 - 1128
Hlavní autoři:	Santos, Eunice E., Murugappan, Vairavan, Korah, John
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.05.2020
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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