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UID:submissions.pasc-conference.org_PASC24_sess156_pos162@linklings.com
SUMMARY:P14 - DynaHGraph: Learning Hidden Relationships in Dynamic Graphs
DESCRIPTION:Poster\n\nKurtis Shuler and Lekha Patel (Sandia National Labor
 atories)\n\nDynamic graphs, whose topologies are defined by a time-evolvin
 g set of nodes or entities and corresponding edges or relationships betwee
 n such entities, are an important field of study across many scientific do
 mains.  Often, it is desirable to learn graph topologies when nodes and ed
 ges in the graph’s topology are only partially observed across time.  Unco
 vering these unknown relationships between known and new entities can be f
 ramed as a link prediction problem of a time-varying, partially observed g
 raphical network.  In this work, we propose a modeling strategy to learn a
  dynamic graph’s underlying structure with quantifiable uncertainties, whe
 n connections in the graph are only partially known.  Using this framework
 , we learn the graph's changing topology via a Markov process and demonstr
 ate how it can be used to predict trajectories of the partially observed d
 ynamic graph. We further discuss the computational challenges of such an a
 pproach, and how they might be overcome within a scientific computing fram
 ework.\n\nSession Chair: Erik W. Draeger (Lawrence Livermore National Labo
 ratory)
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