1 |
Dong, Y., Chawla, N. V., and Swami, A. (2017). MetaPath2Vec: Scalable Representation Learning for Heterogeneous Networks. New York, NY, USA. Association for Computing Machinery.
|
|
2 |
Fu, X., Zhang, J., Meng, Z., and King, I. (2020). MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. WWW ’20, New York, NY, USA.
|
|
3 |
Hamilton, W. L., Ying, R., and Leskovec, J. (2017). Inductive Representation Learning on Large Graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 1025–1035, Red Hook, NY, USA. Curran Associates Inc.
|
|
4 |
Mohd Razali, N. and Yap, B. (2011). Power comparisons of shapiro-wilk, kolmogorovsmirnov, lilliefors and anderson-darling tests. J. Stat. Model. Analytics, 2.
|
|
5 |
Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., and Yu, P. S. (2023). A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources. IEEE Transactions on Big Data, 9(2):415–436.
|
|
6 |
Wu, S., Sun, F., Zhang, W., Xie, X., and Cui, B. (2022). Graph Neural Networks in Recommender Systems: A Survey. ACM Comput. Surv., 55(5).
|
|
7 |
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J. (2018). Graph Convolutional Neural Networks for Web-Scale Recommender Systems.
|
|
8 |
Zhang, C., Song, D., Huang, C., Swami, A., and Chawla, N. V. (2019). Heterogeneous Graph Neural Network. New York, NY, USA. Association for Computing Machinery.
|
|