Quick Start

API Usage

You can run all kinds of experiments through CogDL APIs, especially experiment(). You can also use your own datasets and models for experiments. A quickstart example can be found in the quick_start.py. More examples are provided in the examples/.

from cogdl import experiment

# basic usage
experiment(task="node_classification", dataset="cora", model="gcn")

# set other hyper-parameters
experiment(task="node_classification", dataset="cora", model="gcn", hidden_size=32, max_epoch=200)

# run over multiple models on different seeds
experiment(task="node_classification", dataset="cora", model=["gcn", "gat"], seed=[1, 2])

# automl usage
def func_search(trial):
    return {
        "lr": trial.suggest_categorical("lr", [1e-3, 5e-3, 1e-2]),
        "hidden_size": trial.suggest_categorical("hidden_size", [32, 64, 128]),
        "dropout": trial.suggest_uniform("dropout", 0.5, 0.8),

experiment(task="node_classification", dataset="cora", model="gcn", seed=[1, 2], func_search=func_search)

Command-Line Usage

You can also use python scripts/train.py --task example_task --dataset example_dataset --model example_model to run example_model on example_data and evaluate it via example_task.

  • --task, downstream tasks to evaluate representation like node_classification, unsupervised_node_classification, graph_classification. More tasks can be found in the cogdl/tasks.
  • --dataset, dataset name to run, can be a list of datasets with space like cora citeseer ppi. Supported datasets include ‘cora’, ‘citeseer’, ‘pumbed’, ‘ppi’, ‘wikipedia’, ‘blogcatalog’, ‘flickr’. More datasets can be found in the cogdl/datasets.
  • --model, model name to run, can be a list of models like deepwalk line prone. Supported models include ‘gcn’, ‘gat’, ‘graphsage’, ‘deepwalk’, ‘node2vec’, ‘hope’, ‘grarep’, ‘netmf’, ‘netsmf’, ‘prone’. More models can be found in the cogdl/models.

For example, if you want to run LINE, NetMF on Wikipedia with unsupervised node classification task, with 5 different seeds:

python scripts/train.py --task unsupervised_node_classification --dataset wikipedia --model line netmf --seed 0 1 2 3 4

Expected output:

Variant Micro-F1 0.1 Micro-F1 0.3 Micro-F1 0.5 Micro-F1 0.7 Micro-F1 0.9
(‘wikipedia’, ‘line’) 0.4069±0.0011 0.4071±0.0010 0.4055±0.0013 0.4054±0.0020 0.4080±0.0042
(‘wikipedia’, ‘netmf’) 0.4551±0.0024 0.4932±0.0022 0.5046±0.0017 0.5084±0.0057 0.5125±0.0035

If you want to run parallel experiments on your server with multiple GPUs on multiple models, GCN and GAT, on the Cora dataset with node classification task:

python scripts/parallel_train.py --task node_classification --dataset cora --model gcn gat --device-id 0 1 --seed 0 1 2 3 4

Expected output:

Variant Acc
(‘cora’, ‘gcn’) 0.8236±0.0033
(‘cora’, ‘gat’) 0.8262±0.0032

Fast-Spmm Usage

CogDL provides a fast sparse matrix-matrix multiplication operator called GE-SpMM to speed up training of GNN models on the GPU. You can set fast_spmm=True in the API usage or --fast-spmm in the command-line usage to enable this feature. Note that this feature is still in testing and may not work under some versions of CUDA.