GraphSAGE: Inductive Representation Learning on Large Graphs¶
GraphSAGE is a general inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, GraphSAGE learns a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Based on PGL, we reproduce GraphSAGE algorithm and reach the same level of indicators as the paper in Reddit Dataset. Besides, this is an example of subgraph sampling and training in PGL.
Datasets¶
The reddit dataset should be downloaded from the following links and placed in directory ./data
. The details for Reddit Dataset can be found here.
Dependencies¶
paddlepaddle>=1.6
pgl
How to run¶
To train a GraphSAGE model on Reddit Dataset, you can just run
python train.py --use_cuda --epoch 10 --graphsage_type graphsage_mean --normalize --symmetry
If you want to train a GraphSAGE model with multiple GPUs, you can just run
CUDA_VISIBLE_DEVICES=0,1 python train_multi.py --use_cuda --epoch 10 --graphsage_type graphsage_mean --normalize --symmetry --num_trainer 2
Hyperparameters¶
epoch: Number of epochs default (10)
use_cuda: Use gpu if assign use_cuda.
graphsage_type: We support 4 aggregator types including “graphsage_mean”, “graphsage_maxpool”, “graphsage_meanpool” and “graphsage_lstm”.
normalize: Normalize the input feature if assign normalize.
sample_workers: The number of workers for multiprocessing subgraph sample.
lr: Learning rate.
symmetry: Make the edges symmetric if assign symmetry.
batch_size: Batch size.
samples_1: The max neighbors for the first hop neighbor sampling. (default: 25)
samples_2: The max neighbors for the second hop neighbor sampling. (default: 10)
hidden_size: The hidden size of the GraphSAGE models.
Performance¶
We train our models for 200 epochs and report the accuracy on the test dataset.
Aggregator |
Accuracy |
Reported in paper |
---|---|---|
Mean |
95.70% |
95.0% |
Meanpool |
95.60% |
94.8% |
Maxpool |
94.95% |
94.8% |
LSTM |
95.13% |
95.4% |