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GRADES/NDA@SIGMOD 2022: Philadelphia, PA, USA
- Vasiliki Kalavri, Semih Salihoglu:
GRADES-NDA '22: Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), Philadelphia, Pennsylvania, USA, 12 June 2022. ACM 2022, ISBN 978-1-4503-9384-3 - James A. Hendler:
Knowledge graph semantics. 1:1 - Jing Huang:
Knowledge graph representation learning and graph neural networks for language understanding. 2:1 - Lin Zhao, Arijit Khan
, Robby Luo:
ShaderNet: graph-based shader code analysis to accelerate GPU's performance improvement. 3:1-3:5 - Yann Ramusat, Silviu Maniu
, Pierre Senellart:
Efficient provenance-aware querying of graph databases with datalog. 4:1-4:9 - Kasra Jamshidi
, Mugilan Mariappan, Keval Vora
:
Anti-vertex for neighborhood constraints in subgraph queries. 5:1-5:9 - Mingyu Guan, Anand Padmanabha Iyer, Taesoo Kim:
DynaGraph: dynamic graph neural networks at scale. 6:1-6:10 - Andrew McCrabb, Hellina Nigatu, Absalat Getachew, Valeria Bertacco:
DyGraph: a dynamic graph generator and benchmark suite. 7:1-7:8 - Michael Schramm
, Sukanya Bhowmik, Kurt Rothermel:
Flexible application-aware approximation for modern distributed graph processing frameworks. 8:1-8:10 - Kasimir Gabert, Ali Pinar, Ümit V. Çatalyürek:
Batch dynamic algorithm to find k-core hierarchies. 9:1-9:10 - Shahrzad Khayatbashi, Sebastián Ferrada, Olaf Hartig:
Converting property graphs to RDF: a preliminary study of the practical impact of different mappings. 10:1-10:9 - Renzo Angles, Aidan Hogan, Ora Lassila, Carlos Rojas, Daniel Schwabe, Pedro A. Szekely, Domagoj Vrgoc:
Multilayer graphs: a unified data model for graph databases. 11:1-11:6
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