MPLP: Metapath-Based Label Propagation for Heterogenous Graphs

Related tags

Deep Learningmplp
Overview

MPLP: Metapath-Based Label Propagation for Heterogenous Graphs

Results on MAG240M

Here, we demonstrate the following performance on the MAG240M dataset from [email protected] 2021.

Model Test Acc Validation Acc Parameters Hardware
Our Model 0.7447 0.7669 ± 0.0003 (ensemble 0.7696) 743,449 Tesla V100 (21GB)

Reproducing results

0. Requirements

Here just list python3 packages we used in this competition:

numpy==1.19.2
torch==1.5.1+cu101
dgl-cu101==0.6.0.post1
ogb==1.3.1
sklearn==0.23.2
tqdm==4.46.1

1. Prepare Graph and Features

The preprocess code modifed from dgl baseline. We created graph with 6 different edge types instead of 5.

# Time cost: 3hours,30mins

python3 $MAG_CODE_PATH/preprocess.py
        --rootdir $MAG_INPUT_PATH \
        --author-output-path $MAG_PREP_PATH/author.npy \
        --inst-output-path $MAG_PREP_PATH/inst.npy \
        --graph-output-path $MAG_PREP_PATH \
        --graph-as-homogeneous \
        --full-output-path $MAG_PREP_PATH/full_feat.npy

The graphs and features will be saved in MAG_PREP_PATH , where the MAG_PREP_PATH is specified in run.sh.

Calculate features

The meta-path based features are generated by this script. Details can be found in our technical report.

# Time cost: 2hours,20mins (only generate label related features)

python3 $MAG_CODE_PATH/feature.py
        $MAG_INPUT_PATH \
        $MAG_PREP_PATH/dgl_graph_full_heterogeneous_csr.bin \
        $MAG_FEAT_PATH \
        --seed=42

Train RGAT model and prepare RGAT features

The RGAT model is modifed from dgl baseline. The validation accuracy is 0.701 , as same as described in the dgl baseline github.

# Time cost: 33hours,40mins (20mins for each epoch)

python3 $MAG_CODE_PATH/rgat.py
        --rootdir $MAG_INPUT_PATH \
        --graph-path $MAG_PREP_PATH/dgl_graph_full_homogeneous_csc.bin \
        --full-feature-path $MAG_PREP_PATH/full_feat.npy \
        --output-path $MAG_RGAT_PATH/ \
        --epochs=100 \
        --model-path $MAG_RGAT_PATH/model.pt \
        --submission-path $MAG_RGAT_PATH/

You will get embeddings as input features of the following MPLP models.

2. Train MPLP models

The train process splits to two steps:

  1. train the model with full train samples at a large learning rate (here we use StepLR(lr=0.01, step_size=100, gamma=0.25))
  2. then fine tune the model with latest train samples (eg, paper with year >= 2018) with a small learning rate (0.000625)

You can train the MPLP model by running the following commands:

# Time cost: 2hours,40mins for each seed

for seed in $(seq 0 7);
do
    python3 $MAG_CODE_PATH/mplp.py \
            $MAG_INPUT_PATH \
            $MAG_MPLP_PATH/data/ \
            $MAG_MPLP_PATH/output/seed${seed} \
            --gpu \
            --seed=${seed} \
            --batch_size=10240 \
            --epochs=200 \
            --num_layers=2 \
            --learning_rate=0.01 \
            --dropout=0.5 \
            --num_splits=5
done

3. Ensemble MPLP results

While having all the results with k-fold cross validation training under 8 different seeds, you can average the results by running code below:

python3 $MAG_CODE_PATH/ensemble.py $MAG_MPLP_PATH/output/ $MAG_SUBM_PATH
Owner
Qiuying Peng
Astrophysics -> Data Science
Qiuying Peng
GitHub Repository
(NeurIPS 2021) Realistic Evaluation of Transductive Few-Shot Learning

Realistic evaluation of transductive few-shot learning Introduction This repo contains the code for our NeurIPS 2021 submitted paper "Realistic evalua

Olivier Veilleux 14 Dec 13, 2022
YuNetのPythonでのONNX、TensorFlow-Lite推論サンプル

YuNet-ONNX-TFLite-Sample YuNetのPythonでのONNX、TensorFlow-Lite推論サンプルです。 TensorFlow-LiteモデルはPINTO0309/PINTO_model_zoo/144_YuNetのものを使用しています。 Requirement Op

KazuhitoTakahashi 8 Nov 17, 2021
repro_eval is a collection of measures to evaluate the reproducibility/replicability of system-oriented IR experiments

repro_eval repro_eval is a collection of measures to evaluate the reproducibility/replicability of system-oriented IR experiments. The measures were d

IR Group at Technische Hochschule Köln 9 May 25, 2022
Official code for paper "Optimization for Oriented Object Detection via Representation Invariance Loss".

Optimization for Oriented Object Detection via Representation Invariance Loss By Qi Ming, Zhiqiang Zhou, Lingjuan Miao, Xue Yang, and Yunpeng Dong. Th

ming71 56 Nov 28, 2022
A Real-World Benchmark for Reinforcement Learning based Recommender System

RL4RS: A Real-World Benchmark for Reinforcement Learning based Recommender System RL4RS is a real-world deep reinforcement learning recommender system

121 Dec 01, 2022
Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"

Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Ro

Meta Research 1.2k Jan 02, 2023
Code repository for EMNLP 2021 paper 'Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods'

Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods This is the code repository to accompany the EMNLP 2021 paper on ad

Peru Bhardwaj 7 Sep 25, 2022
'A C2C E-COMMERCE TRUST MODEL BASED ON REPUTATION' Python implementation

Project description A library providing functionalities to calculate reputation and degree of trust on C2C ecommerce platforms. The work is fully base

Davide Bigotti 2 Dec 14, 2022
Writeups for the challenges from DownUnderCTF 2021

cloud Challenge Author Difficulty Release Round Bad Bucket Blue Alder easy round 1 Not as Bad Bucket Blue Alder easy round 1 Lost n Found Blue Alder m

DownUnderCTF 161 Dec 31, 2022
Pytorch Implementation of "Diagonal Attention and Style-based GAN for Content-Style disentanglement in image generation and translation" (ICCV 2021)

DiagonalGAN Official Pytorch Implementation of "Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Trans

32 Dec 06, 2022
Eth brownie struct encoding example

eth-brownie struct encoding example Overview This repository contains an example of encoding a struct, so that it can be used in a function call, usin

Ittai Svidler 2 Mar 04, 2022
PyTorch implementation of Asymmetric Siamese (https://arxiv.org/abs/2204.00613)

Asym-Siam: On the Importance of Asymmetry for Siamese Representation Learning This is a PyTorch implementation of the Asym-Siam paper, CVPR 2022: @inp

Meta Research 89 Dec 18, 2022
The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021

Directed Graph Contrastive Learning The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL). In this paper, we present the first con

Tong Zekun 28 Jan 08, 2023
A list of all papers and resoureces on Semantic Segmentation

Semantic-Segmentation A list of all papers and resoureces on Semantic Segmentation. Dataset importance SemanticSegmentation_DL Some implementation of

Alan Tang 1.1k Dec 12, 2022
Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

English | 简体中文 | 繁體中文 | 한국어 State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained models

Clara Meister 50 Nov 12, 2022
STBP is a way to train SNN with datasets by Backward propagation.

Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Stron

Ling Zhang 18 Dec 09, 2022
🛠 All-in-one web-based IDE specialized for machine learning and data science.

All-in-one web-based development environment for machine learning Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issu

Machine Learning Tooling 2.9k Jan 09, 2023
Implementation of "Semi-supervised Domain Adaptive Structure Learning"

Semi-supervised Domain Adaptive Structure Learning - ASDA This repo contains the source code and dataset for our ASDA paper. Illustration of the propo

3 Dec 13, 2021
GT China coal model

GT China coal model The full version of a China coal transport model with a very high spatial reslution. What it does The code works in a few steps: T

0 Dec 13, 2021
InterfaceGAN++: Exploring the limits of InterfaceGAN

InterfaceGAN++: Exploring the limits of InterfaceGAN Authors: Apavou Clément & Belkada Younes From left to right - Images generated using styleGAN and

Younes Belkada 42 Dec 23, 2022