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DTSTART;TZID=America/Denver:20231115T100000
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UID:submissions.supercomputing.org_SC23_sess303_rpost188@linklings.com
SUMMARY:Scaling K-Path Centrality Using Optimized Distributed Data Structu
 re
DESCRIPTION:Lance Fletcher (Texas A&M University, Lawrence Livermore Natio
 nal Laboratory (LLNL)); Trevor Steil (Lawrence Livermore National Laborato
 ry (LLNL)); and Roger Pearce (Lawrence Livermore National Laboratory (LLNL
 ), Texas A&M University)\n\nK-Path centrality is based on the flow of info
 rmation in a graph along simple paths of length at most K. This work addre
 sses the computational cost of estimating K-path centrality in large-scale
  graphs by introducing the random neighbor traversal graph (RaNT-Graph). T
 he distributed graph data structure employs a combination of vertex delega
 tion partitioning and rejection sampling, enabling it to sample massive am
 ounts of random paths on large scale-free graphs. We evaluate our approach
  by running experiments which demonstrate strong scaling on large real-wor
 ld graphs. The RaNT-Graph approach achieved a 56,544x speedup over the bas
 eline 1D partition implementation when estimating K-path centrality on a g
 raph with 89 million vertices and 1.9 billion edges.\n\nTag: Artificial In
 telligence/Machine Learning, Architecture and Networks, Heterogeneous Comp
 uting, I/O and File Systems, Performance Measurement, Modeling, and Tools,
  Post-Moore Computing, Programming Frameworks and System Software, Quantum
  Computing\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg P
 ass\n\n
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