Heterogeneous Network

Hyper-network

1. Structural Deep Embedding for Hyper-Networksx [AAAI 2018]
2. Hypergraph Neural Networks. [AAAI 2019]
3. Graph HyperNetworks for Neural Architecture Search [ICLR 2019]

Link prediction

4. Modeling Relational Data with Graph Convolutional Networks [2017]
5. Link Prediction via Subgraph Embedding-Based Convex Matrix Completion [AAAI 2018]
6. Graph Convolutional Matrix Completion [KDD 2018]

Heterogenous network
唐杰在KDD Intelligent Feed Recommendation workshop上给的一个keynote slide下载。
异构网络表示学习的source and code

7. metapath2vec: Scalable Representation Learning for Heterogeneous Networks [KDD 2017]
8. Heterogeneous Graph Attention Network [WWW 2019]
9. Representation Learning for Attributed Multiplex Heterogeneous Network [KDD 2019]
10. ActiveHNE: Active Heterogeneous Network Embedding [IJCAI 2019]
11. GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks [IJCAI 2019]
12. HetGNN: Heterogeneous Graph Neural Network [KDD 2019]
13. Adversarial Learning on Heterogeneous Information Networks [KDD 2019]
14. OAG: Toward Linking Large-scale Heterogeneous Entity Graphs [KDD 2019]
15. Metapath2vec

Biologic network

15. GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization [KDD 2019]
16. NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions [bioinformatics 2019] https://github.com/FangpingWan/NeoDTI
17. Yong Liu, Min Wu, Chunyan Miao, Peilin Zhao, and Xiao-Li Li. 2016. Neighborhood regularized logistic matrix factorization for drug-target interaction
    prediction. PLoS computational biology 12, 2 (2016), e1004760.
18. Pham T, Tran T, Venkatesh S. Graph memory networks
    for molecular activity prediction. In: 24th International
    Conference on Pattern Recognition (ICPR) 2018, 639–44. IEEE,
    Beijing.
    github.com/simonfqy/PADME
19. Gao KY, Fokoue A, Luo H, et al. Interpretable drug target prediction using deep neural representation. In: IJCAI.
    Stockholmsmassan: IJCAI, 2018, 3371–7.
    github.com/IBM/InterpretableDTIP

Recommendation

16: KGAT: Knowledge Graph Attention Network for Recommendation [KDD 2019]

Edge Attention-based Multi-Relational Graph Convolutional Networks (2018)
Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi and Jinbo Bi
Paper：https://arxiv.org/abs/1802.04944v1
Python Reference：https://github.com/Luckick/EAGCN

蚂蚁金服在CIKM 2018上的一篇论文《Heterogeneous Graph Neural Networks for Malicious Account Detection》