ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2306.04107
  4. Cited By
BeMap: Balanced Message Passing for Fair Graph Neural Network

BeMap: Balanced Message Passing for Fair Graph Neural Network

7 June 2023
Xiao Lin
Jian Kang
Weilin Cong
Hanghang Tong
    MoE
ArXivPDFHTML

Papers citing "BeMap: Balanced Message Passing for Fair Graph Neural Network"

5 / 5 papers shown
Title
How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark
How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark
Ruizhong Qiu
Weiliang Will Zeng
Hanghang Tong
James Ezick
Christopher Lott
82
15
0
20 Feb 2025
Fairness-aware Message Passing for Graph Neural Networks
Fairness-aware Message Passing for Graph Neural Networks
Huaisheng Zhu
Guoji Fu
Zhimeng Guo
Zhiwei Zhang
Teng Xiao
Suhang Wang
11
6
0
19 Jun 2023
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
Weijing Shi
Ragunathan
R. Rajkumar
3DPC
128
615
0
02 Mar 2020
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
283
4,143
0
23 Aug 2019
SMOTE: Synthetic Minority Over-sampling Technique
SMOTE: Synthetic Minority Over-sampling Technique
Nitesh V. Chawla
Kevin W. Bowyer
Lawrence Hall
W. Kegelmeyer
AI4TS
148
25,150
0
09 Jun 2011
1