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NeuralSparse-PyTorch

This repository provides a PyTorch implementation of the Graph Sparsification technique from the paper Robust Graph Representation Learning via Neural Sparsification. The implementation is applied to the OGBN-Proteins dataset.

NOTE: For this implementation, I've sampled edges from immediate neighbors and not 1-hop neighbors due to the needs of the dataset (edge embeddings aren't available for every edge).

Installation

To install the required dependencies, run:

pip install -r requirements.txt

Usage

To train the model using graph sparsification, execute:

python main.py

To train the model without graph sparsification, execute:

python main.py --mode normal

Results

The table below shows the performance of the model with various sparsification and sampling methods. The results are based on the ROC AUC metric, which is a common evaluation metric for graph representation learning tasks.

TABLE I: System parameters while training

Sparsification
Method		Sampling
Method		CPU
Usage (%)		CPU
Power (W)		GPU
Usage (%)		GPU
Power (W)		RAM
(MB)		vRAM
(MB)
NoneUniformly Random29.610.26.34.342121450
NoneGraphSAGE40.918.615.65.06481433
NoneGraphSAINT45.020.123.79.871211055
DropEdgeUniformly Random40.318.042.820.031413388
DropEdgeGraphSAGE44.019.541.819.367782560
DropEdgeGraphSAINT47.221.223.99.37391849
NeuralSparseUniformly Random36.514.227.416.430741639
NeuralSparseGraphSAGE38.916.024.913.468022955
NeuralSparseGraphSAINT36.814.622.99.177131943

TABLE II: Training results

Sparsification
Method		Sampling
Method		Time per
Epoch (s)		Number of
Epochs		ROC
AUC (%)		EDP (W min²)
NoneUniformly Random18.14966.563605.21
NoneGraphSAGE14.72968.271726.45
NoneGraphSAINT1.44572.2348.29
DropEdgeUniformly Random36.41764.156413.80
DropEdgeGraphSAGE28.23874.7619553.45
DropEdgeGraphSAINT1.83170.5339.01
NeuralSparseUniformly Random106.22576.4681455.40
NeuralSparseGraphSAGE84.11977.3429008.15
NeuralSparseGraphSAINT57.13779.1746494.81

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Implementation of NeuralSparse in PyTorch

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NeuralSparse-PyTorch
This repository provides a PyTorch implementation of the Graph Sparsification technique from the paper Robust Graph Representation Learning via Neural Sparsification. The implementation is applied to the OGBN-Proteins dataset.
NOTE: For this implementation, I've sampled edges from immediate neighbors and not 1-hop neighbors due to the needs of the dataset (edge embeddings aren't available for every edge).
Installation
To install the required dependencies, run:
pip install -r requirements.txt
Usage
To train the model using graph sparsification, execute:
python main.py
To train the model without graph sparsification, execute:
python main.py --mode normal
Results
The table below shows the performance of the model with various sparsification and sampling methods. The results are based on the ROC AUC metric, which is a common evaluation metric for graph representation learning tasks.
TABLE I: System parameters while training
Sparsification
Method		Sampling
Method		CPU
Usage (%)		CPU
Power (W)		GPU
Usage (%)		GPU
Power (W)		RAM
(MB)		vRAM
(MB)
NoneUniformly Random29.610.26.34.342121450
NoneGraphSAGE40.918.615.65.06481433
NoneGraphSAINT45.020.123.79.871211055
DropEdgeUniformly Random40.318.042.820.031413388
DropEdgeGraphSAGE44.019.541.819.367782560
DropEdgeGraphSAINT47.221.223.99.37391849
NeuralSparseUniformly Random36.514.227.416.430741639
NeuralSparseGraphSAGE38.916.024.913.468022955
NeuralSparseGraphSAINT36.814.622.99.177131943
TABLE II: Training results
Sparsification
Method		Sampling
Method		Time per
Epoch (s)		Number of
Epochs		ROC
AUC (%)		EDP (W min²)
NoneUniformly Random18.14966.563605.21
NoneGraphSAGE14.72968.271726.45
NoneGraphSAINT1.44572.2348.29
DropEdgeUniformly Random36.41764.156413.80
DropEdgeGraphSAGE28.23874.7619553.45
DropEdgeGraphSAINT1.83170.5339.01
NeuralSparseUniformly Random106.22576.4681455.40
NeuralSparseGraphSAGE84.11977.3429008.15
NeuralSparseGraphSAINT57.13779.1746494.81
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Implementation of NeuralSparse in PyTorch
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NeuralSparse-PyTorch
This repository provides a PyTorch implementation of the Graph Sparsification technique from the paper Robust Graph Representation Learning via Neural Sparsification. The implementation is applied to the OGBN-Proteins dataset.
NOTE: For this implementation, I've sampled edges from immediate neighbors and not 1-hop neighbors due to the needs of the dataset (edge embeddings aren't available for every edge).
Installation
To install the required dependencies, run:
pip install -r requirements.txt
Usage
To train the model using graph sparsification, execute:
python main.py
To train the model without graph sparsification, execute:
python main.py --mode normal
Results
The table below shows the performance of the model with various sparsification and sampling methods. The results are based on the ROC AUC metric, which is a common evaluation metric for graph representation learning tasks.
TABLE I: System parameters while training
Sparsification
Method		Sampling
Method		CPU
Usage (%)		CPU
Power (W)		GPU
Usage (%)		GPU
Power (W)		RAM
(MB)		vRAM
(MB)
NoneUniformly Random29.610.26.34.342121450
NoneGraphSAGE40.918.615.65.06481433
NoneGraphSAINT45.020.123.79.871211055
DropEdgeUniformly Random40.318.042.820.031413388
DropEdgeGraphSAGE44.019.541.819.367782560
DropEdgeGraphSAINT47.221.223.99.37391849
NeuralSparseUniformly Random36.514.227.416.430741639
NeuralSparseGraphSAGE38.916.024.913.468022955
NeuralSparseGraphSAINT36.814.622.99.177131943
TABLE II: Training results
Sparsification
Method		Sampling
Method		Time per
Epoch (s)		Number of
Epochs		ROC
AUC (%)		EDP (W min²)
NoneUniformly Random18.14966.563605.21
NoneGraphSAGE14.72968.271726.45
NoneGraphSAINT1.44572.2348.29
DropEdgeUniformly Random36.41764.156413.80
DropEdgeGraphSAGE28.23874.7619553.45
DropEdgeGraphSAINT1.83170.5339.01
NeuralSparseUniformly Random106.22576.4681455.40
NeuralSparseGraphSAGE84.11977.3429008.15
NeuralSparseGraphSAINT57.13779.1746494.81
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Implementation of NeuralSparse in PyTorch
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NeuralSparse-PyTorch
This repository provides a PyTorch implementation of the Graph Sparsification technique from the paper Robust Graph Representation Learning via Neural Sparsification. The implementation is applied to the OGBN-Proteins dataset.
NOTE: For this implementation, I've sampled edges from immediate neighbors and not 1-hop neighbors due to the needs of the dataset (edge embeddings aren't available for every edge).
Installation
To install the required dependencies, run:
pip install -r requirements.txt
Usage
To train the model using graph sparsification, execute:
python main.py
To train the model without graph sparsification, execute:
python main.py --mode normal
Results
The table below shows the performance of the model with various sparsification and sampling methods. The results are based on the ROC AUC metric, which is a common evaluation metric for graph representation learning tasks.
TABLE I: System parameters while training
Sparsification
Method		Sampling
Method		CPU
Usage (%)		CPU
Power (W)		GPU
Usage (%)		GPU
Power (W)		RAM
(MB)		vRAM
(MB)
NoneUniformly Random29.610.26.34.342121450
NoneGraphSAGE40.918.615.65.06481433
NoneGraphSAINT45.020.123.79.871211055
DropEdgeUniformly Random40.318.042.820.031413388
DropEdgeGraphSAGE44.019.541.819.367782560
DropEdgeGraphSAINT47.221.223.99.37391849
NeuralSparseUniformly Random36.514.227.416.430741639
NeuralSparseGraphSAGE38.916.024.913.468022955
NeuralSparseGraphSAINT36.814.622.99.177131943
TABLE II: Training results
Sparsification
Method		Sampling
Method		Time per
Epoch (s)		Number of
Epochs		ROC
AUC (%)		EDP (W min²)
NoneUniformly Random18.14966.563605.21
NoneGraphSAGE14.72968.271726.45
NoneGraphSAINT1.44572.2348.29
DropEdgeUniformly Random36.41764.156413.80
DropEdgeGraphSAGE28.23874.7619553.45
DropEdgeGraphSAINT1.83170.5339.01
NeuralSparseUniformly Random106.22576.4681455.40
NeuralSparseGraphSAGE84.11977.3429008.15
NeuralSparseGraphSAINT57.13779.1746494.81
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Implementation of NeuralSparse in PyTorch
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NeuralSparse-PyTorch
This repository provides a PyTorch implementation of the Graph Sparsification technique from the paper Robust Graph Representation Learning via Neural Sparsification. The implementation is applied to the OGBN-Proteins dataset.
NOTE: For this implementation, I've sampled edges from immediate neighbors and not 1-hop neighbors due to the needs of the dataset (edge embeddings aren't available for every edge).
Installation
To install the required dependencies, run:
pip install -r requirements.txt
Usage
To train the model using graph sparsification, execute:
python main.py
To train the model without graph sparsification, execute:
python main.py --mode normal
Results
The table below shows the performance of the model with various sparsification and sampling methods. The results are based on the ROC AUC metric, which is a common evaluation metric for graph representation learning tasks.
TABLE I: System parameters while training
Sparsification
Method		Sampling
Method		CPU
Usage (%)		CPU
Power (W)		GPU
Usage (%)		GPU
Power (W)		RAM
(MB)		vRAM
(MB)
NoneUniformly Random29.610.26.34.342121450
NoneGraphSAGE40.918.615.65.06481433
NoneGraphSAINT45.020.123.79.871211055
DropEdgeUniformly Random40.318.042.820.031413388
DropEdgeGraphSAGE44.019.541.819.367782560
DropEdgeGraphSAINT47.221.223.99.37391849
NeuralSparseUniformly Random36.514.227.416.430741639
NeuralSparseGraphSAGE38.916.024.913.468022955
NeuralSparseGraphSAINT36.814.622.99.177131943
TABLE II: Training results
Sparsification
Method		Sampling
Method		Time per
Epoch (s)		Number of
Epochs		ROC
AUC (%)		EDP (W min²)
NoneUniformly Random18.14966.563605.21
NoneGraphSAGE14.72968.271726.45
NoneGraphSAINT1.44572.2348.29
DropEdgeUniformly Random36.41764.156413.80
DropEdgeGraphSAGE28.23874.7619553.45
DropEdgeGraphSAINT1.83170.5339.01
NeuralSparseUniformly Random106.22576.4681455.40
NeuralSparseGraphSAGE84.11977.3429008.15
NeuralSparseGraphSAINT57.13779.1746494.81
About
Implementation of NeuralSparse in PyTorch
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NeuralSparse-PyTorch
This repository provides a PyTorch implementation of the Graph Sparsification technique from the paper Robust Graph Representation Learning via Neural Sparsification. The implementation is applied to the OGBN-Proteins dataset.
NOTE: For this implementation, I've sampled edges from immediate neighbors and not 1-hop neighbors due to the needs of the dataset (edge embeddings aren't available for every edge).
Installation
To install the required dependencies, run:
pip install -r requirements.txt
Usage
To train the model using graph sparsification, execute:
python main.py
To train the model without graph sparsification, execute:
python main.py --mode normal
Results
The table below shows the performance of the model with various sparsification and sampling methods. The results are based on the ROC AUC metric, which is a common evaluation metric for graph representation learning tasks.
TABLE I: System parameters while training
Sparsification
Method		Sampling
Method		CPU
Usage (%)		CPU
Power (W)		GPU
Usage (%)		GPU
Power (W)		RAM
(MB)		vRAM
(MB)
NoneUniformly Random29.610.26.34.342121450
NoneGraphSAGE40.918.615.65.06481433
NoneGraphSAINT45.020.123.79.871211055
DropEdgeUniformly Random40.318.042.820.031413388
DropEdgeGraphSAGE44.019.541.819.367782560
DropEdgeGraphSAINT47.221.223.99.37391849
NeuralSparseUniformly Random36.514.227.416.430741639
NeuralSparseGraphSAGE38.916.024.913.468022955
NeuralSparseGraphSAINT36.814.622.99.177131943
TABLE II: Training results
Sparsification
Method		Sampling
Method		Time per
Epoch (s)		Number of
Epochs		ROC
AUC (%)		EDP (W min²)
NoneUniformly Random18.14966.563605.21
NoneGraphSAGE14.72968.271726.45
NoneGraphSAINT1.44572.2348.29
DropEdgeUniformly Random36.41764.156413.80
DropEdgeGraphSAGE28.23874.7619553.45
DropEdgeGraphSAINT1.83170.5339.01
NeuralSparseUniformly Random106.22576.4681455.40
NeuralSparseGraphSAGE84.11977.3429008.15
NeuralSparseGraphSAINT57.13779.1746494.81
About
Implementation of NeuralSparse in PyTorch
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NeuralSparse-PyTorch
This repository provides a PyTorch implementation of the Graph Sparsification technique from the paper Robust Graph Representation Learning via Neural Sparsification. The implementation is applied to the OGBN-Proteins dataset.
NOTE: For this implementation, I've sampled edges from immediate neighbors and not 1-hop neighbors due to the needs of the dataset (edge embeddings aren't available for every edge).
Installation
To install the required dependencies, run:
pip install -r requirements.txt
Usage
To train the model using graph sparsification, execute:
python main.py
To train the model without graph sparsification, execute:
python main.py --mode normal
Results
The table below shows the performance of the model with various sparsification and sampling methods. The results are based on the ROC AUC metric, which is a common evaluation metric for graph representation learning tasks.
TABLE I: System parameters while training
Sparsification
Method		Sampling
Method		CPU
Usage (%)		CPU
Power (W)		GPU
Usage (%)		GPU
Power (W)		RAM
(MB)		vRAM
(MB)
NoneUniformly Random29.610.26.34.342121450
NoneGraphSAGE40.918.615.65.06481433
NoneGraphSAINT45.020.123.79.871211055
DropEdgeUniformly Random40.318.042.820.031413388
DropEdgeGraphSAGE44.019.541.819.367782560
DropEdgeGraphSAINT47.221.223.99.37391849
NeuralSparseUniformly Random36.514.227.416.430741639
NeuralSparseGraphSAGE38.916.024.913.468022955
NeuralSparseGraphSAINT36.814.622.99.177131943
TABLE II: Training results
Sparsification
Method		Sampling
Method		Time per
Epoch (s)		Number of
Epochs		ROC
AUC (%)		EDP (W min²)
NoneUniformly Random18.14966.563605.21
NoneGraphSAGE14.72968.271726.45
NoneGraphSAINT1.44572.2348.29
DropEdgeUniformly Random36.41764.156413.80
DropEdgeGraphSAGE28.23874.7619553.45
DropEdgeGraphSAINT1.83170.5339.01
NeuralSparseUniformly Random106.22576.4681455.40
NeuralSparseGraphSAGE84.11977.3429008.15
NeuralSparseGraphSAINT57.13779.1746494.81
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Implementation of NeuralSparse in PyTorch
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NeuralSparse-PyTorch
This repository provides a PyTorch implementation of the Graph Sparsification technique from the paper Robust Graph Representation Learning via Neural Sparsification. The implementation is applied to the OGBN-Proteins dataset.
NOTE: For this implementation, I've sampled edges from immediate neighbors and not 1-hop neighbors due to the needs of the dataset (edge embeddings aren't available for every edge).
Installation
To install the required dependencies, run:
pip install -r requirements.txt
Usage
To train the model using graph sparsification, execute:
python main.py
To train the model without graph sparsification, execute:
python main.py --mode normal
Results
The table below shows the performance of the model with various sparsification and sampling methods. The results are based on the ROC AUC metric, which is a common evaluation metric for graph representation learning tasks.
TABLE I: System parameters while training
Sparsification
Method		Sampling
Method		CPU
Usage (%)		CPU
Power (W)		GPU
Usage (%)		GPU
Power (W)		RAM
(MB)		vRAM
(MB)
NoneUniformly Random29.610.26.34.342121450
NoneGraphSAGE40.918.615.65.06481433
NoneGraphSAINT45.020.123.79.871211055
DropEdgeUniformly Random40.318.042.820.031413388
DropEdgeGraphSAGE44.019.541.819.367782560
DropEdgeGraphSAINT47.221.223.99.37391849
NeuralSparseUniformly Random36.514.227.416.430741639
NeuralSparseGraphSAGE38.916.024.913.468022955
NeuralSparseGraphSAINT36.814.622.99.177131943
TABLE II: Training results
Sparsification
Method		Sampling
Method		Time per
Epoch (s)		Number of
Epochs		ROC
AUC (%)		EDP (W min²)
NoneUniformly Random18.14966.563605.21
NoneGraphSAGE14.72968.271726.45
NoneGraphSAINT1.44572.2348.29
DropEdgeUniformly Random36.41764.156413.80
DropEdgeGraphSAGE28.23874.7619553.45
DropEdgeGraphSAINT1.83170.5339.01
NeuralSparseUniformly Random106.22576.4681455.40
NeuralSparseGraphSAGE84.11977.3429008.15
NeuralSparseGraphSAINT57.13779.1746494.81
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Implementation of NeuralSparse in PyTorch
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