Graph相关(图学习|图神经网络|图优化等)(8篇)
[ 1 ] Object DGCNN: 3D Object Detection using Dynamic Graphs
Object DGCNN：基于动态图的三维目标检测
链接：https://arxiv.org/abs/2110.06923
作者：Yue Wang,Justin Solomon
机构：Massachusetts Institute of Technology
备注：Accepted to NeurIPS 2021
[ 2 ] TAG: Toward Accurate Social Media Content Tagging with a Concept Graph
标签：使用概念图实现准确的社交媒体内容标签
链接：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
机构：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
图欺诈者：基于图神经网络垂直联合学习的对抗性攻击
链接：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
基于字典学习和图形插值的数据驱动供水管网泄漏定位
链接：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
分布式动态图中的增量式社区发现
链接：https://arxiv.org/abs/2110.06311
作者：Tariq Abughofa,Ahmed A. Harby,Haruna Isah,Farhana Zulkernine
机构：School of Computing, Kingston, ON, Canada, Ahmed A.Harby, Queen’s University Kingston
备注：BigDataService 2021 best paper award
[ 6 ] Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
基于自集成自蒸馏的图神经网络可扩展一致性训练
链接：https://arxiv.org/abs/2110.06290
作者：Cole Hawkins,Vassilis N. Ioannidis,Soji Adeshina,George Karypis
机构：University of California, Santa Barbara, Amazon Web Services
[ 7 ] Molecular Graph Generation via Geometric Scattering
基于几何散射的分子图生成
链接：https://arxiv.org/abs/2110.06241
作者：Dhananjay Bhaskar,Jackson D. Grady,Michael A. Perlmutter,Smita Krishnaswamy
机构：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
用图网络学习量子哈密顿量的基态
链接：https://arxiv.org/abs/2110.06390
作者：Dmitrii Kochkov,Tobias Pfaff,Alvaro Sanchez-Gonzalez,Peter Battaglia,Bryan K. Clark
机构：Google Research, DeepMind, University of Illinois at Urbana-Champaign
备注：19 pages, 9 figures