Graph相關(圖學習|圖神經網絡|圖優化等)(8篇)
[ 1 ] Object DGCNN: 3D Object Detection using Dynamic Graphs
Object DGCNN：基于動态圖的三維目标檢測
連結：https://arxiv.org/abs/2110.06923
作者：Yue Wang,Justin Solomon
機構：Massachusetts Institute of Technology
備注：Accepted to NeurIPS 2021
[ 2 ] TAG: Toward Accurate Social Media Content Tagging with a Concept Graph
标簽：使用概念圖實作準确的社交媒體内容标簽
連結：https://arxiv.org/abs/2110.06892
作者：Jiuding Yang,Weidong Guo,Bang Liu,Yakun Yu,Chaoyue Wang,Jinwen Luo,Linglong Kong,Di Niu,Zhen Wen
機構：University of Alberta, Edmonton, AB, Canada, RALI & Mila, Université de Montréal, Montréal, QC, Canada, Platform and Content Group, Tencent, Beijing, China
[ 3 ] Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning
圖欺詐者：基于圖神經網絡垂直聯合學習的對抗性攻擊
連結：https://arxiv.org/abs/2110.06468
作者：Jinyin Chen,Guohan Huang,Shanqing Yu,Wenrong Jiang,Chen Cui
[ 4 ] Data-driven Leak Localization in Water Distribution Networks via Dictionary Learning and Graph-based Interpolation
基于字典學習和圖形插值的資料驅動供水管網洩漏定位
連結：https://arxiv.org/abs/2110.06372
作者：Paul Irofti,Luis Romero-Ben,Florin Stoican,Vicenç Puig
[ 5 ] Incremental Community Detection in Distributed Dynamic Graph
分布式動态圖中的增量式社群發現
連結：https://arxiv.org/abs/2110.06311
作者：Tariq Abughofa,Ahmed A. Harby,Haruna Isah,Farhana Zulkernine
機構：School of Computing, Kingston, ON, Canada, Ahmed A.Harby, Queen’s University Kingston
備注：BigDataService 2021 best paper award
[ 6 ] Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
基于自內建自蒸餾的圖神經網絡可擴充一緻性訓練
連結：https://arxiv.org/abs/2110.06290
作者：Cole Hawkins,Vassilis N. Ioannidis,Soji Adeshina,George Karypis
機構：University of California, Santa Barbara, Amazon Web Services
[ 7 ] Molecular Graph Generation via Geometric Scattering
基于幾何散射的分子圖生成
連結：https://arxiv.org/abs/2110.06241
作者：Dhananjay Bhaskar,Jackson D. Grady,Michael A. Perlmutter,Smita Krishnaswamy
機構：Department of Genetics, Yale University, New Haven, CT , USA, Department of Computer Science, Department of Mathematics, UCLA, Los Angeles, CA , USA
[ 8 ] Learning ground states of quantum Hamiltonians with graph networks
用圖網絡學習量子哈密頓量的基态
連結：https://arxiv.org/abs/2110.06390
作者：Dmitrii Kochkov,Tobias Pfaff,Alvaro Sanchez-Gonzalez,Peter Battaglia,Bryan K. Clark
機構：Google Research, DeepMind, University of Illinois at Urbana-Champaign
備注：19 pages, 9 figures
因上求緣，果上努力~~~~ 作者：每天卷學習，轉載請注明原文連結：https://www.cnblogs.com/BlairGrowing/p/15416589.html