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English Information

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Authors
# Name
1 Silvio Angonese(sfangonese@inf.ufrgs.br)
2 Renata Galante(galante@inf.ufrgs.br)

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Reference
# Reference
1 Angonese, S. F. and Galante, R. (2024). AGHE: Approach for Generating Enhanced Heterogeneous Embeddings from Heterogeneous Graphs. In 51º Semin´ario Integrado de Software e Hardware (SEMISH), Brasilia, DF, Brazil.
2 Dong, Y., Chawla, N. V., and Swami, A. (2017). MetaPath2Vec: Scalable Representation Learning for Heterogeneous Networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, page 135–144, New York, NY, USA. Association for Computing Machinery.
3 Fu, X., Zhang, J., Meng, Z., and King, I. (2020). MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. In Proceedings of The Web Conference 2020, WWW ’20, page 2331–2341, New York, NY, USA.
4 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.
5 Jin, D., Huo, C., Liang, C., and Yang, L. (2021). Heterogeneous Graph Neural Network via Attribute Completion. In Proceedings of the Web Conference 2021, WWW ’21, page 391–400, New York, NY, USA. Association for Computing Machinery.
6 Mohd Razali, N. and Yap, B. (2011). Power comparisons of shapiro-wilk, kolmogorov smirnov, lilliefors and anderson-darling tests. J. Stat. Model. Analytics, 2.
7 Santana, D. and Ribeiro, L. (2023). Approximate similarity joins over dense vector em beddings. In Anais do XXXVIII Simp´osio Brasileiro de Bancos de Dados, pages 51–62, Porto Alegre, RS, Brasil. SBC.
8 Sun, Y. and Han, J. (2012). Mining Heterogeneous Information Networks: Principles and Methodologies. Morgan amp; Claypool Publishers.
9 Sun, Y., Han, J., Yan, X., Yu, P. S., and Wu, T. (2020). PathSim: Meta Path-Based Top K Similarity Search in Heterogeneous Information Networks. Proc. VLDB Endow., 4(11):992–1003.
10 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.
11 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).
12 Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J. (2018). Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery amp; Data Mining, KDD ’18, page 974–983, New York, NY, USA. Association for Computing Machinery.
13 Zhang, C., Song, D., Huang, C., Swami, A., and Chawla, N. V. (2019). Heterogeneous Graph Neural Network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery amp; Data Mining, KDD ’19, page 793–803, New York, NY, USA. Association for Computing Machinery.