Implementation for Simple Spectral Graph Convolution in ICLR 2021

Related tags

Deep Learning SSGC
Overview

Simple Spectral Graph Convolutional

Overview

This repo contains an example implementation of the Simple Spectral Graph Convolutional (S^2GC) model. This code is based on SGC. We will update the code in Text Classification and Node Clustering latter.

SGC removes the nonlinearities and collapes the weight matrices in Graph Convolutional Networks (GCNs) and is essentially a linear model. However, it also cannot beat GCNs in most benchmarks. S^2GC is a new baseline method for GCNs and downstream applications.

S^2GC achieves competitive performance while saving much training time, basically the computational cost is very close to SGC.

Dataset Metric
Cora Acc: 83.0 %
Citeseer Acc: 73.6 %
Pubmed Acc: 80.6 %
Reddit F1: 95.3 %

This home repo contains the implementation for citation networks (Cora, Citeseer, and Pubmed) and social network (Reddit).

Dependencies

Our implementation works with PyTorch>=1.0.0 Install other dependencies: $ pip install -r requirement.txt

Data

We provide the citation network datasets under data/, which corresponds to the public data splits.

Usage

$ python citation_cora.py
$ python citation_citeseer.py 
$ python citation_pubmed.py
Comments
  • Citations Hyperparameter Search

    Citations Hyperparameter Search

    Hi, I'm a researcher looking at reproducing the results of the hyperparameter search and I'm struggling to get values similar to those in the paper. The implementations here have a few errors (I'm not sure if this is to do with pytorch version updates, but I don't think so...), so I've created PR #14 which makes things runnable. However, the results it generates are significantly worse than those published. Can you provide additional details about the hyperparameter search you conducted, or guidance on where I've gone wrong?

    Thanks in advance.

    opened by jackd 11
  • Question about reproducing test accuracy on citation datasets

    Question about reproducing test accuracy on citation datasets

    Hi, I have read your paper, downloaded the code, and ran it without any changes (I follow the same hyperparameters and random seeds). But I found that I can't reproduce the accuracy reported in the README.md file.

    My results (dataset, test accuracy) are:

    cora, 82.4%
citeseer, 73.0%
pubmed, 80.0%

    My version:

    PyTorch=1.7.1 with cuda
networkx=1.11
numpy=1.19.2
scipy=1.5.2
scikit-learn=0.24.1
hyperopt=0.1.1

    did I miss some information or do something wrong?

    opened by Youth-49 3
  • tuning fix + added repeats

    tuning fix + added repeats

    This PR contains 3 main changes (probably should have been 3 PRs, but I'm not particularly fussed about whether it gets accepted or not - I'm mostly opening so that I can reference it from an issue).

    • added item as per #7
    • wrapped main functionality of citations_XXX in a loop and added repeats to args_XXX
    • fixed errors with tuning_XXX

    I've also added the results of hyperparameter tuning and --tuned runs to README.md, though there seems to be some missing seed controls to make this reproducible. Note that results are significantly worse than those reported.

    opened by jackd 0
  • The pyg implement of SSGC

    The pyg implement of SSGC

    Hi, I noticed an inconsistency between the paper and this implementation. Equation 13 in the paper, x_final = alpha * x_ori + (1 - alpha) * mean(x_list); But in the line 101 of ssgc-mlp.py, x_final = alpha * x_ori + mean(x_list). Could you tell me what causes the difference?

    opened by Haldate-Yu 1
  • OGB Hyperparameters

    OGB Hyperparameters

    Hello, are there any specific hyperparameters one should use when trying to reproduce the OGB node classification results? I extended the approach from the cora, citeseer, and pubmed dataset to the OGB-arxiv dataset but only achieve ~50% accuracy and not 70% as stated in the paper. This is especially weird because the cora, citeseer, and pubmed results are all agreeing with the ones in the paper. Also, your link to the preprocessed files is not working ("https://drive.google.com/drive/u/0/my-drive").

    opened by Timob12 3
  • 20NG Training Set Reported Results Question

    20NG Training Set Reported Results Question

    For your 20NG results reported in your original paper (88.6± 0.1), was the model trained on the full public 20NG train set, of size 11314, or were the reported results generated using the training code currently in this repo here and here, which appears to exclude the validation set from the data used to train the model? (The latter would use a training set of size 10183).

    Thanks!

    opened by jharrang 3
Owner
allenhaozhu
PhD Student
allenhaozhu
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