MGNN: Graph Neural Networks Inspired by Distance Geometry Problem-Reference-Cited by-同舟云学术

MGNN: Graph Neural Networks Inspired by Distance Geometry Problem

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Cui Guanyu¹^ORCID,Wei Zhewei¹^ORCID

Affiliation:

1. Renmin University of China, Beijing, China

Funder

Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education

Beijing Outstanding Young Scientist Program

Intelligent Social Governance Interdisciplinary Platform, Major Innovation & Planning Interdisciplinary Platform for the ?Double-First Class? Initiative, Public Policy and Decision-making Research Lab, Public Computing Cloud, Renmin University of China

Beijing Natural Science Foundation

Huawei-Renmin University joint program on Information Retrieval

National Natural Science Foundation of China

the major key project of PCL

Alibaba Group through Alibaba Innovative Research Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599431

Reference64 articles.

1. Abdo Y Alfakih . 2007. On dimensional rigidity of bar-and-joint frameworks. Discrete applied mathematics , Vol. 155 , 10 ( 2007 ), 1244--1253. Abdo Y Alfakih. 2007. On dimensional rigidity of bar-and-joint frameworks. Discrete applied mathematics, Vol. 155, 10 (2007), 1244--1253.

2. Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions

3. Muhammet Balcilar , Guillaume Renton , Pierre Héroux , Benoit Gaüzè re, Sébastien Adam , and Paul Honeine . 2021 . Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective . In 9th International Conference on Learning Representations, ICLR 2021 , Virtual Event, Austria , May 3-7, 2021. OpenReview.net. https://openreview.net/forum?id=-qh0M9XWxnv Muhammet Balcilar, Guillaume Renton, Pierre Héroux, Benoit Gaüzè re, Sébastien Adam, and Paul Honeine. 2021. Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https://openreview.net/forum?id=-qh0M9XWxnv

4. Semidefinite programming based algorithms for sensor network localization

5. Beyond Low-frequency Information in Graph Convolutional Networks