('user','-1','movie') : minus1}) 使用ACM异质图数据集: importscipy.ioimporturllib.request data_url='https://data.dgl.ai/dataset/ACM.mat'data_file_path='/tmp/ACM.mat'urllib.request.urlretrieve(data_url, data_file_path) data=scipy.io.loadmat(data_file_path)print(list(data.keys()))...
import dgl import scipy.io import urllib.request data_url = 'https://data.dgl.ai/dataset/ACM.mat' data_file_path = '/tmp/ACM.mat' urllib.request.urlretrieve(data_url, data_file_path) data = scipy.io.loadmat(data_file_path) print(list(data.keys())) ### # The dataset stores node...
15. 使用ACM异质图数据集: import scipy.io import urllib.request data_url = 'https://data.dgl.ai/dataset/ACM.mat' data_file_path = '/tmp/ACM.mat' urllib.request.urlretrieve(data_url, data_file_path) data = scipy.io.loadmat(data_file_path) print(list(data.keys())) 1. 2. 3. 4....
import dglimport scipy.ioimport urllib.requestdata_url = 'https://data.dgl.ai/dataset/ACM.mat'data_file_path = '/tmp/ACM.mat'urllib.request.urlretrieve(data_url, data_file_path)data = scipy.io.loadmat(data_file_path)print(list(data.keys()))### The dataset stores node information by ...
You cancreate a more realistic heterographusing the ACM dataset. To do this, first download the dataset as follows: importscipy.ioimporturllib.request data_url='https://s3.us-east-2.amazonaws.com/dgl.ai/dataset/ACM.mat'data_file_path='/tmp/ACM.mat'urllib.request.urlretrieve(data_url,data...
Our paper [OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network](https://dl.acm.org/doi/abs/10.1145/3511808.3557664) is accpeted at CIKM 2022 short paper track. 2022-06-27 release v0.3 We release the latest version v0.3. ...
DGLBACKEND=pytorch dglke_train --model_name TransE_l2 --dataset FB15k --batch_size 1000 \ --neg_sample_size 200 --hidden_dim 400 --gamma 19.9 --lr 0.25 --max_step 500 --log_interval 100 \ --batch_size_eval 16 -adv --regularization_coef 1.00E-09 --test --num_thread 1 --num...
train_mask, val_mask, test_maskdefload_acm_raw(remove_self_loop):assertnotremove_self_loop url='dataset/ACM.mat'data_path= get_download_dir() +'/ACM.mat'download(_get_dgl_url(url), path=data_path) data=sio.loadmat(data_path) ...
data_url = 'https://data.dgl.ai/dataset/ACM.mat' data_file_path = '/tmp/ACM.mat' urllib.request.urlretrieve(data_url, data_file_path) data = scipy.io.loadmat(data_file_path) print(list(data.keys())) ### # The dataset stores node information by their types: ``P`` for paper,...
usage: main.py [-h] [--model MODEL] [--task TASK] [--dataset DATASET] [--gpu GPU] [--use_best_config][--use_database]optional arguments:-h, --help show this help message and exit--model -m name of models--task -t name of task...