Web: http://arxiv.org/abs/2104.00186

Sept. 19, 2022, 1:12 a.m. | Zixun Lan, Limin Yu, Linglong Yuan, Zili Wu, Qiang Niu, Fei Ma

cs.LG updates on arXiv.org arxiv.org

As one of the most fundamental tasks in graph theory, subgraph matching is a
crucial task in many fields, ranging from information retrieval, computer
vision, biology, chemistry and natural language processing. Yet subgraph
matching problem remains to be an NP-complete problem. This study proposes an
end-to-end learning-based approximate method for subgraph matching task, called
subgraph matching network (Sub-GMN). The proposed Sub-GMN firstly uses graph
representation learning to map nodes to node-level embedding. It then combines
metric learning and attention mechanisms …

arxiv network

More from arxiv.org / cs.LG updates on arXiv.org

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Data Scientist (Analytics) - Singapore

@ Momos | Singapore, Central, Singapore

Machine Learning Scientist, Drug Discovery

@ Flagship Pioneering, Inc. | Cambridge, MA

Applied Scientist - Computer Vision

@ Flawless | Los Angeles, California, United States

Sr. Data Engineer, Customer Service

@ Wayfair Inc. | Boston, MA