A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

Graph Analysis & Deep Learning Laboratory, GRAND

Last update: Jan 2, 2023

Overview

MERIT

A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning.

Dependencies

Python (>=3.6)
PyTorch (>=1.7.1)
NumPy (>=1.19.2)
Scikit-Learn (>=0.24.1)
Scipy (>=1.6.1)
Networkx (>=2.5)

To install all dependencies:

pip install -r requirements.txt

Usage

Here we provide the implementation of MERIT along with Cora and Citeseer dataset.

To train and evaluate on Cora:

python run_cora.py

To train and evaluate on Citeseer:

python run_citeseer.py

Citation

If you use our code in your research, please cite the following article:

@inproceedings{Jin2021MultiScaleCS,
  title={Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning},
  author={Ming Jin and Yizhen Zheng and Yuan-Fang Li and Chen Gong and Chuan Zhou and Shirui Pan},
  booktitle={The 30th International Joint Conference on Artificial Intelligence (IJCAI)},
  year={2021}
}

Comments

Why is the acc different from the MVGRL paper?

Hello! In your paper, your acc of MVGRL method on three datasets (Cora, CiteSeer, PubMed) are 82.9, 72.6, 79.4. But in MVGRL paper, the acc are 86.8, 73.3, 80.1. I think both of you use the same dataset, so could you please explain the reason?

opened by hcmdgh 1
Some confusion about the hyper-parameters of pubmed dataset

Hi, author. I read your paper and codes, but I don't find the illustration about the hper-parameters of pubmed dataset. Could you please answer my question? Thank you!

opened by MrsYaoH 1
Bump joblib from 1.0.1 to 1.2.0
Bumps joblib from 1.0.1 to 1.2.0.

Changelog

Sourced from joblib's changelog.

Release 1.2.0

Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide joblib/joblib#1256

Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. joblib/joblib#1263

Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with mmap_mode != None as the resulting numpy.memmap object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. joblib/joblib#1254

Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.

Vendor loky 3.3.0 which fixes several bugs including:

robustly forcibly terminating worker processes in case of a crash (joblib/joblib#1269);

avoiding leaking worker processes in case of nested loky parallel calls;

reliability spawn the correct number of reusable workers.

Release 1.1.1

Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

Release 1.1.0

Fix byte order inconsistency issue during deserialization using joblib.load

... (truncated)

Commits

5991350 Release 1.2.0

3fa2188 MAINT cleanup numpy warnings related to np.matrix in tests (#1340)

cea26ff CI test the future loky-3.3.0 branch (#1338)

8aca6f4 MAINT: remove pytest.warns(None) warnings in pytest 7 (#1264)

067ed4f XFAIL test_child_raises_parent_exits_cleanly with multiprocessing (#1339)

ac4ebd5 MAINT add back pytest warnings plugin (#1337)

a23427d Test child raises parent exits cleanly more reliable on macos (#1335)

ac09691 [MAINT] various test updates (#1334)

4a314b1 Vendor loky 3.2.0 (#1333)

bdf47e9 Make test_parallel_with_interactively_defined_functions_default_backend timeo...

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dependencies
opened by dependabot[bot] 0
Bump urllib3 from 1.26.3 to 1.26.5
Bumps urllib3 from 1.26.3 to 1.26.5.

Release notes

Sourced from urllib3's releases.

1.26.5

:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap

Fixed deprecation warnings emitted in Python 3.10.

Updated vendored six library to 1.16.0.

Improved performance of URL parser when splitting the authority component.

If you or your organization rely on urllib3 consider supporting us via GitHub Sponsors

1.26.4

:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap

Changed behavior of the default SSLContext when connecting to HTTPS proxy during HTTPS requests. The default SSLContext now sets check_hostname=True.

If you or your organization rely on urllib3 consider supporting us via GitHub Sponsors

Changelog

Sourced from urllib3's changelog.

1.26.5 (2021-05-26)

Fixed deprecation warnings emitted in Python 3.10.

Updated vendored six library to 1.16.0.

Improved performance of URL parser when splitting the authority component.

1.26.4 (2021-03-15)

Changed behavior of the default SSLContext when connecting to HTTPS proxy during HTTPS requests. The default SSLContext now sets check_hostname=True.

Commits

d161647 Release 1.26.5

2d4a3fe Improve performance of sub-authority splitting in URL

2698537 Update vendored six to 1.16.0

07bed79 Fix deprecation warnings for Python 3.10 ssl module

d725a9b Add Python 3.10 to GitHub Actions

339ad34 Use pytest==6.2.4 on Python 3.10+

f271c9c Apply latest Black formatting

1884878 [1.26] Properly proxy EOF on the SSLTransport test suite

a891304 Release 1.26.4

8d65ea1 Merge pull request from GHSA-5phf-pp7p-vc2r

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dependencies
opened by dependabot[bot] 0
Questions about accuracy.

According to the implementation of other codes such as DGI, GRACE, etc., they all use the results of the last epoch or the minimum loss in the training process (early stopping) to calculate acc as the result. And your code is to calculate acc every 10 rounds, and use the best acc among all acc as your experimental result.

I think contrastive learning is unsupervised learning, and their codes are correct. It is unfair to compare the acc of your codes with them.

opened by nnnnnzy 0
Could you please support the hyper-parameters of the other datasets, ie. Amazon Photo and Coauthor CS?

Hi, author. Because Amazon and Coauthor data is immense, I have no idea to handle your hyper-parameters. Could you please support the hyper-parameters about these two datasets? Thank you for your work. Sincerely.

opened by MrsYaoH 0

Owner

Graph Analysis & Deep Learning Laboratory, GRAND

GRaph ANalysis & Deep learning Laboratory (GRAND Lab) at Monash University

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A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

Related tags

Overview

MERIT

Dependencies

Usage

Citation

Comments

Why is the acc different from the MVGRL paper?

Some confusion about the hyper-parameters of pubmed dataset

Bump joblib from 1.0.1 to 1.2.0

Release 1.2.0

Release 1.1.1

Release 1.1.0

Bump urllib3 from 1.26.3 to 1.26.5

1.26.5

1.26.4

1.26.5 (2021-05-26)

1.26.4 (2021-03-15)

Questions about accuracy.

Could you please support the hyper-parameters of the other datasets, ie. Amazon Photo and Coauthor CS?

Owner

Graph Analysis & Deep Learning Laboratory, GRAND

Official PyTorch implementation of "RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on" (IJCAI-ECAI 2022)

Official PyTorch implementation of SyntaSpeech (IJCAI 2022)

Disentangled Face Attribute Editing via Instance-Aware Latent Space Search, accepted by IJCAI 2021.

[IJCAI-2021] A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation"

[IJCAI'21] Deep Automatic Natural Image Matting

Code for the IJCAI 2021 paper "Structure Guided Lane Detection"

Omnidirectional Scene Text Detection with Sequential-free Box Discretization (IJCAI 2019). Including competition model, online demo, etc.

DTCN IJCAI - Sequential prediction learning framework and algorithm

MGFN: Multi-Graph Fusion Networks for Urban Region Embedding was accepted by IJCAI-2022.

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RETRO-pytorch - Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch

HashNeRF-pytorch - Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives