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DTSTAMP:20260202T201805Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20251118T080000
DTEND;TZID=America/Chicago:20251118T170000
UID:submissions.supercomputing.org_SC25_sess527_post298@linklings.com
SUMMARY:GNNs on Evolving Graphs: A Benchmark of Incremental Updates and Me
 ta-Learning Approaches
DESCRIPTION:Sriram Srinivasan (Bowie State University), Sanjukta Bhowmick 
 (University of North Texas), and Hamdan Alabsi and Rand Obeidat (Bowie Sta
 te University)\n\nThe field of graph machine learning has seen significant
  growth with the success of graph neural networks (GCNs). However, most tr
 aditional GCNs are designed for static graphs. In the real world, graphs a
 re constantly evolving—new users join social networks, molecules change sh
 ape, and data streams into a network. Re-computing a GCN's embeddings for 
 the entire graph every time a small change occurs is computationally expen
 sive and inefficient. This research explores two more efficient approaches
 : a standard incremental update method and a novel meta-learning approach,
  which are then benchmarked to compare their performance.\n\nTag: Research
  & ACM SRC Posters\n\nRegistration Category: Technical Program Reg Pass\n\
 nSession Chairs: Kento Sato (RIKEN Center for Computational Science (R-CCS
 )); Chris Schlipalius (Pawsey Supercomputing Research Centre; Commonwealth
  Scientific and Industrial Research Organisation (CSIRO), Australia); and 
 Anja Gerbes (Georg-August-Universität Göttingen)\n\n
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