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UID:submissions.supercomputing.org_SC24_sess487_post217@linklings.com
SUMMARY:Efficient Approaches to Analyzing Large Dynamic Networks
DESCRIPTION:Arindam Khanda and S.M. Shovan (Missouri University of Science
  and Technology), Aashish Pandey and Farahnaz Hosseini (University of Nort
 h Texas), Sajal K. Das (Missouri University of Science and Technology), Bo
 yana Norris (University of Oregon), and Sanjukta Bhowmick (University of N
 orth Texas)\n\nDynamic graphs, characterized by their evolving topologies 
 over time, necessitate continuous updates to their associated graph proper
 ties, including shortest paths, vertex coloring, and strongly connected co
 mponents. Traditional static graph algorithms, which re-compute properties
  following each modification, typically falter in efficiency under such co
 nditions. In this paper, we introduce a suite of methodologies implemented
  within our software platform, CANDY, designed to efficiently analyze dyna
 mic graphs. We propose a generic framework that supports the parallel upda
 ting of graph properties across large networks subject to various types of
  changes. Our results demonstrate the enhanced performance of these update
  algorithms in managing large dynamic networks, highlighting significant i
 mprovements over conventional approaches.\n\nRegistration Category: Tech P
 rogram Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha Afzal (Friedr
 ich-Alexander-Universität Erlangen-Nürnberg, Erlangen National High Perfor
 mance Computing Center); Sally Ellingson (University of Kentucky); and Ala
 n Sussman (University of Maryland)\n\n
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