A PyTorch implementation of the Relational Graph Convolutional Network (RGCN).

Related tags

Deep Learning link-prediction graph-convolutional-network node-classification graph-neural-network relational-graphs
Overview

Torch-RGCN

Torch-RGCN is a PyTorch implementation of the RGCN, originally proposed by Schlichtkrull et al. in
Modeling Relational Data with Graph Convolutional Networks.

In our paper, we reproduce the link prediction and node classification experiments from the original paper and using our reproduction we explain the RGCN. Furthermore, we present two new configurations of the RGCN.

Getting started

Requirements:

  • Conda >= 4.8
  • Python >= 3.7

Do the following:

  1. Download all datasets: bash get_data.sh

  2. Install the dependencies inside a new virtual environment: bash setup_dependencies.sh

  3. Activate the virtual environment: conda activate torch_rgcn_venv

  4. Install the torch-RGCN module: pip install -e .

Usage

Configuration files

The hyper-parameters for the different experiments can be found in YAML files under configs. The naming convention of the files is as follows: configs/{MODEL}/{EXPERIMENT}-{DATASET}.yaml

Models

  • rgcn - Standard RGCN Model
  • c-rgcn - Compression RGCN Model
  • e-rgcn - Embedding RGCN Model

Experiments

  • lp - Link Prediction
  • nc - Node Classification

Datasets

Link Prediction

  • WN18
  • FB-Toy

Node Classification

  • AIFB
  • MUTAG
  • BGS
  • AM

Part 1: Reproduction

Link Prediction

Link Prediction Model

Original Link Prediction Implementation: https://github.com/MichSchli/RelationPrediction

To run the link prediction experiment using the RGCN model using:

python experiments/predict_links.py with configs/rgcn/lp-{DATASET}.yaml

Make sure to replace {DATASET} with one of the following dataset names: FB-toy or WN18.

Node Classification

Node Classification Model

Original Node Classification Implementation: https://github.com/tkipf/relational-gcn

To run the node classification experiment using the RGCN model using:

python experiments/classify_nodes.py with configs/rgcn/nc-{DATASET}.yaml

Make sure to replace {DATASET} with one of the following dataset names: AIFB, MUTAG, BGS or AM.

Part 2: New RGCN Configurations

Node Classification with Node Embeddings

To run the node classification experiment use:

python experiments/classify_nodes.py with configs/e-rgcn/nc-{DATASET}.yaml

Make sure to replace {DATASET} with one of the following dataset names: AIFB, MUTAG, BGS or AM.

Link Prediction Compressed Node Embeddings

c-RGCN Link Prediction Model

To run the link prediction experiment use:

python experiments/predict_links.py with configs/c-rgcn/lp-{DATASET}.yaml

Make sure to replace {DATASET} with one of the following dataset names: FB-toy, or WN18.

Dataset References

Node Classification

Link Prediction

Comments
  • work on entity alignment

    work on entity alignment

    does r-gcn works on entity alignment (existing R-GCN seems only focus on classification and link prediction)?

    If yes, May I know do you have any working code on this?

    or does this link prediction model works directly on that? Since “Links between two nodes exist” is very similar to “two nodes referring to same real-world object”

    opened by sunset1234321 1
  • Error when running the link prediction task using c-rgcn

    Error when running the link prediction task using c-rgcn

    Using the default config file present in /configs/c-rgcn/lp-WN18.yaml after changing the weight initialisation as suggested in #12, I get the following error.

    INFO - R-GCN Link Prediction  - Started
Start training...
ERROR - R-GCN Link Prediction  - Failed after 0:00:02!
Traceback (most recent calls WITHOUT Sacred internals):
  File "experiments/predict_links.py", line 151, in train
    predictions, penalty = model(graph, batch_idx)
  File "/usr/local/envs/torch_rgcn_venv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/drive/MyDrive/research/dr/torch-rgcn/torch_rgcn/models.py", line 235, in forward
    x = self.rgc1(graph, features=x)
  File "/usr/local/envs/torch_rgcn_venv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/drive/MyDrive/research/dr/torch-rgcn/torch_rgcn/layers.py", line 550, in forward
    fw = torch.einsum('ni, rio -> rno', features, weights).contiguous()
  File "/usr/local/envs/torch_rgcn_venv/lib/python3.7/site-packages/torch/functional.py", line 201, in einsum
    return torch._C._VariableFunctions.einsum(equation, operands)
RuntimeError: size of dimension does not match previous size, operand 1, dim 1

    Any help is appreciated, TIA!

    opened by Sidharth3301 1
  • GPU utilization in link prediction task is 0.

    GPU utilization in link prediction task is 0.

    As this issues mentioned, the link prediction code can't run. So I used a history commit b1b6646, it can run normally for link prediction task. But when I use the 'configs/rgcn/rp-WN18.yaml' default config files which set "use_cuda: True" , I found that the running speed is much slower than the node classification task. So I monitored the usage of gpu, I found that the utilization rate of gpu is 0. 1637132062(1) 1637132078(1)

    opened by nnnnnzy 1
  • Error when running the link prediction experiment

    Error when running the link prediction experiment

    I was able to run the node classification on all datasets, unfortunately the link prediction experiment always throws an exception, I've tried changing the configurations but no luck, below is the error:

    ...
WARNING - root - Added new config entry: "training.use_cuda"
WARNING - R-GCN Link Prediction  - No observers have been added to this run
INFO - R-GCN Link Prediction  - Running command 'train'
INFO - R-GCN Link Prediction  - Started
ERROR - R-GCN Link Prediction  - Failed after 0:00:00!
Traceback (most recent calls WITHOUT Sacred internals):
  File "experiments/predict_links.py", line 88, in train
    decoder_config=decoder
  File "/home/raftel/torch-rgcn/torch_rgcn/models.py", line 224, in __init__
    .__init__(nnodes, nrel, nfeat, encoder_config, decoder_config)
  File "/home/raftel/torch-rgcn/torch_rgcn/models.py", line 58, in __init__
    init(self.node_embeddings)
TypeError: schlichtkrull_normal_() missing 1 required positional argument: 'shape'
    opened by raftelo 9
  • triples_plus contain original triples twice

    triples_plus contain original triples twice

    In the forward() function of the RelationalGraphConvolutionLP Layer, the generate_self_loops() function concatenates the triples and self-edges, which are then concatenated again for the triples_plus with the triples and inverse_triples. This way the triples_plus contain the original edges twice. Also, self-edges are created not only for the nodes left in the sampled (and edge-dropped) graph but for all nodes of the original graph. Is this implemented this way on purpose?

    opened by Tauboga208 0
Owner
Thiviyan Singam
PhD candidate at University of Amsterdam
Thiviyan Singam
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