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Ethan's Projects

adagnn icon adagnn

Open-source code for ''Graph Neural Networks with Adaptive Frequency Response Filter''.

bind icon bind

Open-source code for "Interpreting Unfairness in Graph Neural Networks via Training Node Attribution".

covid-datasets icon covid-datasets

Include 6 different datasets from different data sources for different research purposes.

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为15个章节,近20万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06

dig icon dig

A library for graph deep learning research

edits icon edits

Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".

elegant icon elegant

Open-source code for "ELEGANT: Certified Defense on the Fairness of Graph Neural Networks".

ganf icon ganf

Offical implementation of "Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series" (ICLR 2022)

guide icon guide

Implementation of GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks (KDD 2022)

idea icon idea

This the open-source code for KDD' 24 IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks

leetcode icon leetcode

LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解,记录自己的leetcode解题之路。)

nifty icon nifty

Code for paper https://arxiv.org/abs/2102.13186

nrgnn icon nrgnn

Offical pytorch implementation of proposed NRGNN and Compared Methods in "NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs" (KDD 2021).

paper-writing-tips icon paper-writing-tips

该仓库是MLNLP社区用来帮助大家避免论文投稿小错误的整理仓库。 Paper Writing Tips

pygdebias icon pygdebias

Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms

pygod icon pygod

A Python Library for Graph Outlier Detection (Anomaly Detection)

redress icon redress

Open-source code for ''Individual Fairness for Graph Neural Networks: A Ranking based Approach''.

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