AKGNN

The source code for Adaptive Kernel Graph Neural Network at AAAI2022.

Please cite our paper if you think our work is helpful to you:

@inproceedings{ju2022akgnn,
  title={Adaptive Kernel Graph Neural Network},
  author={Ju, Mingxuan and Hou, Shifu and Fan, Yujie and Zhao, Jianan and Ye, Yanfang and Zhao, Liang},
  booktitle={36th AAAI Conference on Artificial Intelligence (AAAI)},
  year={2022}
}

Requirements

Python 3.8.3
Please install other pakeages by pip install -r requirement.txt

Usage Example

Running on Cora: python train_cora.py
Running on Citeseer: python train_citeseer.py
Running on Pubmed: python train_pubmed.py

Results

Our model achieves the following accuracies on Cora, CiteSeer and Pubmed with the public splits:

Model name	Cora	CiteSeer	Pubmed
AKGNN	84.8%	73.5%	80.4%

Running Environment

The experimental results reported in paper are conducted on a single NVIDIA GeForce RTX 2080 Ti with CUDA 11.1, which might be slightly inconsistent with the results induced by other platforms.

The source code for Adaptive Kernel Graph Neural Network at AAAI2022

Related tags

Overview

AKGNN

Requirements

Usage Example

Results

Running Environment

Owner

Datasets, Transforms and Models specific to Computer Vision

Code for ACM MM 2020 paper "NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination"

A python library for time-series smoothing and outlier detection in a vectorized way.

Deeplearning project at The Technological University of Denmark (DTU) about Neural ODEs for finding dynamics in ordinary differential equations and real world time series data

A pyparsing-based library for parsing SOQL statements

Implementation of "A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement" by pytorch

Simple data balancing baselines for worst-group-accuracy benchmarks.

Code for 2021 NeurIPS --- Towards Multi-Grained Explainability for Graph Neural Networks

An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision

Disagreement-Regularized Imitation Learning

A self-supervised 3D representation learning framework named viewpoint bottleneck.

NLP made easy

A very short and easy implementation of Quantile Regression DQN

Implementation for the paper 'YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs'

GrailQA: Strongly Generalizable Question Answering

Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms

Caffe-like explicit model constructor. C(onfig)Model

一个多模态内容理解算法框架，其中包含数据处理、预训练模型、常见模型以及模型加速等模块。

A python implementation of Yolov5 to detect fire or smoke in the wild in Jetson Xavier nx and Jetson nano