LightGCN
Introduction
Title: LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Authors: Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang
Abstract: Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on GCN, which is originally designed for graph classification tasks and equipped with many neural network operations. However, we empirically find that the two most common designs in GCNs – feature transformation and nonlinear activation – contribute little to the performance of collaborative filtering. Even worse, including them adds to the difficulty of training and degrades recommendation performance. In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. We propose a new model named LightGCN, including only the most essential component in GCN – neighborhood aggregation – for collaborative filtering. Specifically, LightGCN learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding. Such simple, linear, and neat model is much easier to implement and train, exhibiting substantial improvements (about 16.0% relative improvement on average) over Neural Graph Collaborative Filtering (NGCF) – a state-of-the-art GCN-based recommender model – under exactly the same experimental setting. Further analyses are provided towards the rationality of the simple LightGCN from both analytical and empirical perspectives.
Running with XGCN
forward_mode: ‘full_graph’
When using the ‘full_graph’ forward_mode, embeddings of all the nodes are inferred in each training batch.
Configuration template:
# config/LightGCN-full_graph-config.yaml
# Dataset/Results root
data_root: ""
results_root: ""
# Trainer configuration
epochs: 200
use_validation_for_early_stop: 1
val_freq: 1
key_score_metric: r100
convergence_threshold: 20
val_method: ""
val_batch_size: 256
file_val_set: ""
# Testing configuration
test_method: ""
test_batch_size: 256
file_test_set: ""
# DataLoader configuration
Dataset_type: NodeListDataset
num_workers: 0
NodeListDataset_type: LinkDataset
pos_sampler: ObservedEdges_Sampler
neg_sampler: RandomNeg_Sampler
num_neg: 1
BatchSampleIndicesGenerator_type: SampleIndicesWithReplacement
train_batch_size: 1024
str_num_total_samples: num_edges
epoch_sample_ratio: 0.1
# Model configuration
model: LightGCN
seed: 1999
graph_device: "cuda:0"
emb_table_device: "cuda:0"
gnn_device: "cuda:0"
out_emb_table_device: "cuda:0"
forward_mode: full_graph
from_pretrained: 0
file_pretrained_emb: ""
freeze_emb: 0
use_sparse: 0
emb_dim: 64
emb_init_std: 0.1
emb_lr: 0.005
num_gcn_layers: 2
stack_layers: 1
loss_type: bpr
L2_reg_weight: 0.0
use_ego_emb_L2_reg: 0
Run from command line:
# script/examples/facebook/run_LightGCN-full_graph.sh
# set to your own path:
all_data_root='/home/sxr/code/XGCN_and_data/XGCN_data'
config_file_root='/home/sxr/code/XGCN_and_data/XGCN_library/config'
dataset=facebook
model=LightGCN
seed=0
device="cuda:1"
graph_device=$device
emb_table_device=$device
gnn_device=$device
out_emb_table_device=$device
data_root=$all_data_root/dataset/instance_$dataset
results_root=$all_data_root/model_output/$dataset/$model/[seed$seed]
# file_pretrained_emb=$all_data_root/model_output/$dataset/Node2vec/[seed$seed]/out_emb_table.pt
python -m XGCN.main.run_model --seed $seed \
--config_file $config_file_root/$model-full_graph-config.yaml \
--data_root $data_root --results_root $results_root \
--val_method one_pos_k_neg \
--file_val_set $data_root/val-one_pos_k_neg.pkl \
--test_method multi_pos_whole_graph \
--file_test_set $data_root/test-multi_pos_whole_graph.pkl \
--graph_device $graph_device --emb_table_device $emb_table_device \
--gnn_device $gnn_device --out_emb_table_device $out_emb_table_device \
# --from_pretrained 1 --file_pretrained_emb $file_pretrained_emb \
forward_mode: ‘sample’
When using the ‘sample’ forward_mode, DGL’s neighbor sampler is used to generate “blocks” (please refer to DGL docs: Chapter 6: Stochastic Training on Large Graphs for more information).
# config/LightGCN-block-config.yaml
# Dataset/Results root
data_root: ""
results_root: ""
# Trainer configuration
epochs: 200
use_validation_for_early_stop: 1
val_freq: 1
key_score_metric: r100
convergence_threshold: 20
val_method: ""
val_batch_size: 256
file_val_set: ""
# Testing configuration
test_method: ""
test_batch_size: 256
file_test_set: ""
# DataLoader configuration
Dataset_type: BlockDataset
num_workers: 0
num_gcn_layers: 2
train_num_layer_sample: "[10, 20]"
NodeListDataset_type: LinkDataset
pos_sampler: ObservedEdges_Sampler
neg_sampler: RandomNeg_Sampler
num_neg: 1
BatchSampleIndicesGenerator_type: SampleIndicesWithReplacement
train_batch_size: 1024
str_num_total_samples: num_edges
epoch_sample_ratio: 0.1
# Model configuration
model: LightGCN
seed: 1999
graph_device: "cuda:0"
emb_table_device: "cuda:0"
gnn_device: "cuda:0"
out_emb_table_device: "cuda:0"
forward_mode: sample
infer_num_layer_sample: "[10, 20]"
from_pretrained: 0
file_pretrained_emb: ""
freeze_emb: 0
use_sparse: 0
emb_dim: 64
emb_init_std: 0.1
emb_lr: 0.005
stack_layers: 1
loss_type: bpr
L2_reg_weight: 0.0
use_ego_emb_L2_reg: 0
Run from command line:
# script/examples/facebook/run_LightGCN-block.sh
# set to your own path:
all_data_root='/home/sxr/code/XGCN_and_data/XGCN_data'
config_file_root='/home/sxr/code/XGCN_and_data/XGCN_library/config'
dataset=facebook
model=LightGCN
seed=0
device="cuda:1"
graph_device=$device
emb_table_device=$device
gnn_device=$device
out_emb_table_device=$device
data_root=$all_data_root/dataset/instance_$dataset
results_root=$all_data_root/model_output/$dataset/$model/[seed$seed]
# file_pretrained_emb=$all_data_root/model_output/$dataset/Node2vec/[seed$seed]/out_emb_table.pt
python -m XGCN.main.run_model --seed $seed \
--config_file $config_file_root/$model-block-config.yaml \
--data_root $data_root --results_root $results_root \
--val_method one_pos_k_neg \
--file_val_set $data_root/val-one_pos_k_neg.pkl \
--test_method multi_pos_whole_graph \
--file_test_set $data_root/test-multi_pos_whole_graph.pkl \
--graph_device $graph_device --emb_table_device $emb_table_device \
--gnn_device $gnn_device --out_emb_table_device $out_emb_table_device \
# --from_pretrained 1 --file_pretrained_emb $file_pretrained_emb \