BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260422T143139Z
LOCATION:B314
DTSTART;TZID=America/New_York:20241117T163000
DTEND;TZID=America/New_York:20241117T170000
UID:submissions.supercomputing.org_SC24_sess741_ws_mlg101@linklings.com
SUMMARY:AM-DGCNN: Leveraging Graph Attention Networks and Edge Attributes 
 for Link Classification in Knowledge Graphs
DESCRIPTION:Dhroov Pandey and Tong Shu (University of North Texas)\n\nGrap
 h-based representations are increasingly popular for storing and managing 
 information through knowledge graphs, which capture entities and their rel
 ationships. However, these knowledge graphs often suffer from incomplete l
 ink information. To address this issue, link classification methods can be
  used to predict and verify missing connections. Recently, supervised heur
 istic learning methods have improved link classification accuracy. Specifi
 cally, the SEAL framework, as a state-of-the-art supervised heuristic lear
 ning tool, excels in learning associativity patterns by analyzing local en
 closing subgraphs to classify links. However, DGCNN, a graph neural networ
 k model in this framework, lacks the capability to process edge attributes
 , leading to poor classification accuracy in knowledge graphs. Hence, this
  paper proposes an Augmented Model of the DGCNN (AM-DGCNN) by replacing GC
 Ns with GATs to better incorporate link information. With extensive experi
 ments, we demonstrate that our AM-DGCNN in the SEAL framework can achieve 
 up to 98% accuracy for classifying links in knowledge graphs.\n\nTag: Arti
 ficial Intelligence/Machine Learning, Graph Algorithms, Scalable Data Mini
 ng\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Seung-Hw
 an Lim (Oak Ridge National Laboratory (ORNL)); José Moreira (IBM); Catheri
 ne Schuman (University of Tennessee, Knoxville); and Richard Vuduc (Georgi
 a Institute of Technology)\n\n
END:VEVENT
END:VCALENDAR