oag-cs - 使用OAG微软学术数据构造的计算机领域的学术网络（见 readme）
（1）验证集
从AMiner发布的 AI 2000人工智能全球最具影响力学者榜单 抓取人工智能20个子领域的top 100学者
pip install scrapy>=2.3.0
cd gnnrec/kgrec/data/preprocess
scrapy runspider ai2000_crawler.py -a save_path=/home/zzy/GNN-Recommendation/data/rank/ai2000.json与oag-cs数据集的学者匹配，并人工确认一些排名较高但未匹配上的学者，作为学者排名ground truth验证集
export DJANGO_SETTINGS_MODULE=academic_graph.settings.common
export SECRET_KEY=xxx
python -m gnnrec.kgrec.data.preprocess.build_author_rank build-val（2）训练集
参考AI 2000的计算公式，根据某个领域的论文引用数加权求和构造学者排名，作为ground truth训练集
计算公式：
python -m gnnrec.kgrec.data.preprocess.build_author_rank build-train --log-citation（3）评估ground truth训练集的质量
python -m gnnrec.kgrec.data.preprocess.build_author_rank build-train --log-citation --use-original-id
python -m gnnrec.kgrec.data.preprocess.build_author_rank evalnDCG@100=0.2420 Precision@100=0.1859 Recall@100=0.2037
nDCG@50=0.2308 Precision@50=0.2494 Recall@50=0.1365
nDCG@20=0.2492 Precision@20=0.3118 Recall@20=0.0685
nDCG@10=0.2743 Precision@10=0.3471 Recall@10=0.0380
nDCG@5=0.3165 Precision@5=0.3765 Recall@5=0.0205
基于图神经网络的学术推荐算法(Graph Neural Network based Academic Recommendation Algorithm, GARec)
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
- 随机游走
python -m gnnrec.kgrec.random_walk model/word2vec/oag_cs_corpus.txt- 训练词向量
python -m gnnrec.hge.metapath2vec.train_word2vec --size=128 --workers=8 model/word2vec/oag_cs_corpus.txt model/word2vec/oag_cs.model使用微调后的SciBERT模型（见 readme 第2步）将查询词编码为向量，与预先计算好的论文标题向量计算余弦相似度，取top k
python -m gnnrec.kgrec.garec.recall论文召回结果示例：
graph neural network
0.9629 Aggregation Graph Neural Networks
0.9579 Neural Graph Learning: Training Neural Networks Using Graphs
0.9556 Heterogeneous Graph Neural Network
0.9552 Neural Graph Machines: Learning Neural Networks Using Graphs
0.9490 On the choice of graph neural network architectures
0.9474 Measuring and Improving the Use of Graph Information in Graph Neural Networks
0.9362 Challenging the generalization capabilities of Graph Neural Networks for network modeling
0.9295 Strategies for Pre-training Graph Neural Networks
0.9142 Supervised Neural Network Models for Processing Graphs
0.9112 Geometrically Principled Connections in Graph Neural Networks
recommendation algorithm based on knowledge graph
0.9172 Research on Video Recommendation Algorithm Based on Knowledge Reasoning of Knowledge Graph
0.8972 An Improved Recommendation Algorithm in Knowledge Network
0.8558 A personalized recommendation algorithm based on interest graph
0.8431 An Improved Recommendation Algorithm Based on Graph Model
0.8334 The Research of Recommendation Algorithm based on Complete Tripartite Graph Model
0.8220 Recommendation Algorithm based on Link Prediction and Domain Knowledge in Retail Transactions
0.8167 Recommendation Algorithm Based on Graph-Model Considering User Background Information
0.8034 A Tripartite Graph Recommendation Algorithm Based on Item Information and User Preference
0.7774 Improvement of TF-IDF Algorithm Based on Knowledge Graph
0.7770 Graph Searching Algorithms for Semantic-Social Recommendation
scholar disambiguation
0.9690 Scholar search-oriented author disambiguation
0.9040 Author name disambiguation in scientific collaboration and mobility cases
0.8901 Exploring author name disambiguation on PubMed-scale
0.8852 Author Name Disambiguation in Heterogeneous Academic Networks
0.8797 KDD Cup 2013: author disambiguation
0.8796 A survey of author name disambiguation techniques: 2010–2016
0.8721 Who is Who: Name Disambiguation in Large-Scale Scientific Literature
0.8660 Use of ResearchGate and Google CSE for author name disambiguation
0.8643 Automatic Methods for Disambiguating Author Names in Bibliographic Data Repositories
0.8641 A brief survey of automatic methods for author name disambiguation
python -m gnnrec.kgrec.garec.train model/word2vec/oag-cs.model model/garec/rhgnn_garec_rank.pt训练完成后得到学者嵌入rank/author_embed.pkl
直接对每个学者的论文得分求和作为学者得分
python -m gnnrec.kgrec.garec.train_sumpython -m gnnrec.kgrec.kgcn.train