Skip to content

Latest commit

 

History

History
135 lines (115 loc) · 6.9 KB

readme.md

File metadata and controls

135 lines (115 loc) · 6.9 KB
Raw

异构图表示学习

数据集

  • ACM - ACM学术网络数据集
  • DBLP - DBLP学术网络数据集
  • ogbn-mag - OGB提供的微软学术数据集
  • oag-venue - oag-cs期刊分类数据集
数据集 顶点数 边数 目标顶点 类别数
ACM 11246 34852 paper 3
DBLP 26128 239566 author 4
ogbn-mag 1939743 21111007 paper 349
oag-venue 4235169 34520417 paper 360

Baselines

R-GCN (full batch)

python -m gnnrec.hge.rgcn.train --dataset=acm --epochs=10
python -m gnnrec.hge.rgcn.train --dataset=dblp --epochs=10
python -m gnnrec.hge.rgcn.train --dataset=ogbn-mag --num-hidden=48
python -m gnnrec.hge.rgcn.train --dataset=oag-venue --num-hidden=48 --epochs=30

（使用minibatch训练准确率就是只有20%多，不知道为什么）

预训练顶点嵌入

使用metapath2vec（随机游走+word2vec）预训练顶点嵌入，作为GNN模型的顶点输入特征

python -m gnnrec.hge.metapath2vec.random_walk model/word2vec/ogbn-mag_corpus.txt
python -m gnnrec.hge.metapath2vec.train_word2vec --size=128 --workers=8 model/word2vec/ogbn-mag_corpus.txt model/word2vec/ogbn-mag.model

HAN

结果来自HeCo论文

HGT

python -m gnnrec.hge.hgt.train_full --dataset=acm
python -m gnnrec.hge.hgt.train_full --dataset=dblp
python -m gnnrec.hge.hgt.train --dataset=ogbn-mag --node-embed-path=model/word2vec/ogbn-mag.model --epochs=40
python -m gnnrec.hge.hgt.train --dataset=oag-venue --node-embed-path=model/word2vec/oag-cs.model --epochs=40

HGConv

python -m gnnrec.hge.hgconv.train_full --dataset=acm --epochs=5
python -m gnnrec.hge.hgconv.train_full --dataset=dblp --epochs=20
python -m gnnrec.hge.hgconv.train --dataset=ogbn-mag --node-embed-path=model/word2vec/ogbn-mag.model
python -m gnnrec.hge.hgconv.train --dataset=oag-venue --node-embed-path=model/word2vec/oag-cs.model

R-HGNN

python -m gnnrec.hge.rhgnn.train_full --dataset=acm --num-layers=1 --epochs=15
python -m gnnrec.hge.rhgnn.train_full --dataset=dblp --epochs=20
python -m gnnrec.hge.rhgnn.train --dataset=ogbn-mag model/word2vec/ogbn-mag.model
python -m gnnrec.hge.rhgnn.train --dataset=oag-venue --epochs=50 model/word2vec/oag-cs.model

C&S

python -m gnnrec.hge.cs.train --dataset=acm --epochs=5
python -m gnnrec.hge.cs.train --dataset=dblp --epochs=5
python -m gnnrec.hge.cs.train --dataset=ogbn-mag --prop-graph=data/graph/pos_graph_ogbn-mag_t5.bin
python -m gnnrec.hge.cs.train --dataset=oag-venue --prop-graph=data/graph/pos_graph_oag-venue_t5.bin

HeCo

python -m gnnrec.hge.heco.train --dataset=ogbn-mag model/word2vec/ogbn-mag.model data/graph/pos_graph_ogbn-mag_t5.bin
python -m gnnrec.hge.heco.train --dataset=oag-venue model/word2vec/oag-cs.model data/graph/pos_graph_oag-venue_t5.bin

（ACM和DBLP的数据来自 https://github.com/ZZy979/pytorch-tutorial/tree/master/gnn/heco ，准确率和Micro-F1相等）

RHCO

基于对比学习的关系感知异构图神经网络(Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning, RHCO)

在HeCo的基础上改进：

  • 网络结构编码器中的注意力向量改为关系的表示（类似于R-HGNN）
  • 正样本选择方式由元路径条数改为预训练的HGT计算的注意力权重、训练集使用真实标签
  • 元路径视图编码器改为正样本图编码器，适配mini-batch训练
  • Loss增加分类损失，训练方式由无监督改为半监督
  • 在最后增加C&S后处理步骤

ACM

python -m gnnrec.hge.hgt.train_full --dataset=acm --save-path=model/hgt/hgt_acm.pt
python -m gnnrec.hge.rhco.build_pos_graph_full --dataset=acm --num-samples=5 --use-label model/hgt/hgt_acm.pt data/graph/pos_graph_acm_t5l.bin
python -m gnnrec.hge.rhco.train_full --dataset=acm data/graph/pos_graph_acm_t5l.bin

DBLP

python -m gnnrec.hge.hgt.train_full --dataset=dblp --save-path=model/hgt/hgt_dblp.pt
python -m gnnrec.hge.rhco.build_pos_graph_full --dataset=dblp --num-samples=5 --use-label model/hgt/hgt_dblp.pt data/graph/pos_graph_dblp_t5l.bin
python -m gnnrec.hge.rhco.train_full --dataset=dblp --use-data-pos data/graph/pos_graph_dblp_t5l.bin

ogbn-mag（第3步如果中断可使用--load-path参数继续训练）

python -m gnnrec.hge.hgt.train --dataset=ogbn-mag --node-embed-path=model/word2vec/ogbn-mag.model --epochs=40 --save-path=model/hgt/hgt_ogbn-mag.pt
python -m gnnrec.hge.rhco.build_pos_graph --dataset=ogbn-mag --num-samples=5 --use-label model/word2vec/ogbn-mag.model model/hgt/hgt_ogbn-mag.pt data/graph/pos_graph_ogbn-mag_t5l.bin
python -m gnnrec.hge.rhco.train --dataset=ogbn-mag --num-hidden=64 --contrast-weight=0.9 model/word2vec/ogbn-mag.model data/graph/pos_graph_ogbn-mag_t5l.bin model/rhco/rhco_ogbn-mag_d64_a0.9_t5l.pt
python -m gnnrec.hge.rhco.smooth --dataset=ogbn-mag model/word2vec/ogbn-mag.model data/graph/pos_graph_ogbn-mag_t5l.bin model/rhco/rhco_ogbn-mag_d64_a0.9_t5l.pt

oag-venue

python -m gnnrec.hge.hgt.train --dataset=oag-venue --node-embed-path=model/word2vec/oag-cs.model --epochs=40 --save-path=model/hgt/hgt_oag-venue.pt
python -m gnnrec.hge.rhco.build_pos_graph --dataset=oag-venue --num-samples=5 --use-label model/word2vec/oag-cs.model model/hgt/hgt_oag-venue.pt data/graph/pos_graph_oag-venue_t5l.bin
python -m gnnrec.hge.rhco.train --dataset=oag-venue --num-hidden=64 --contrast-weight=0.9 model/word2vec/oag-cs.model data/graph/pos_graph_oag-venue_t5l.bin model/rhco/rhco_oag-venue.pt
python -m gnnrec.hge.rhco.smooth --dataset=oag-venue model/word2vec/oag-cs.model data/graph/pos_graph_oag-venue_t5l.bin model/rhco/rhco_oag-venue.pt

消融实验

python -m gnnrec.hge.rhco.train --dataset=ogbn-mag --model=RHCO_sc model/word2vec/ogbn-mag.model data/graph/pos_graph_ogbn-mag_t5l.bin model/rhco/rhco_sc_ogbn-mag.pt
python -m gnnrec.hge.rhco.train --dataset=oag-venue --model=RHCO_sc model/word2vec/oag-cs.model data/graph/pos_graph_oag-venue_t5l.bin model/rhco/rhco_sc_oag-venue.pt
python -m gnnrec.hge.rhco.train --dataset=ogbn-mag --model=RHCO_pg model/word2vec/ogbn-mag.model data/graph/pos_graph_ogbn-mag_t5.bin model/rhco/rhco_pg_ogbn-mag.pt
python -m gnnrec.hge.rhco.train --dataset=oag-venue --model=RHCO_pg model/word2vec/oag-cs.model data/graph/pos_graph_oag-venue_t5.bin model/rhco/rhco_pg_oag-venue.pt

实验结果

顶点分类

参数敏感性分析

消融实验