[2]
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality, in Proc. 26 th Int. Conf. Neural Information Processing Systems, Lake Tahoe, NV, USA, 2013, pp. 3111−3119.
[3]
T. Y. Fu, W. C. Lee, and Z. Lei, HIN2Vec: Explore meta-paths in heterogeneous information networks for representation learning, in Proc. 2017 ACM on Conf. Information and Knowledge Management, Singapore, 2017, pp. 1797–1806.
[4]
C. Yang, Z. Liu, D. Zhao, M. Sun, and E. Y. Chang, Network representation learning with rich text information, in Proc. 24 th Int. Conf. Artificial Intelligence, Buenos Aires, Argentina, 2015, pp. 2111–2117.
[5]
A. Grover and J. Leskovec, node2vec: Scalable feature learning for networks, in Proc. 22 nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 855–864.
[6]
H. Ji, S. Z. He, L. H. Xu, K. Liu, and J. Zhao, Knowledge graph embedding via dynamic mapping matrix, in Proc. 53 rd Annu. Meeting of the Association for Computational Linguistics and the 7 th Int. Joint Conf. Natural Language Processing, Beijing, China, 2015, pp. 687–696.
[7]
Z. Huang, Y. Zheng, R. Cheng, Y. Sun, N. Mamoulis, and X. Li, Meta structure: Computing relevance in large heterogeneous information networks, in Proc. 22 nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 1595–1604.
[8]
J. Shang, M. Qu, J. Liu, L. M. Kaplan, J. Han, and J. Peng, Meta-path guided embedding for similarity search in large-scale heterogeneous information networks, arXiv preprint arXiv: 1610.09769, 2016.
[12]
C. H. Q. Ding, X. He, H. Zha, M. Gu, and H. D. Simon, A min-max cut algorithm for graph partitioning and data clustering, in Proc. 2001 IEEE Int. Conf. Data Mining, San Jose, CA, USA, 2001, pp. 107−114.
[13]
B. Gao, T. Y. Liu, X. Zheng, Q. S. Cheng, and W. Y. Ma, Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering, in Proc. 11 th ACM SIGKDD Int. Conf. Knowledge Discovery in Data Mining, Chicago, IL, USA, 2005, pp. 41–50.
[14]
B. Hendrickson and R. Leland, A multi-level algorithm for partitioning graphs, in Proc. 1995 ACM/IEEE Conf. Supercomputing, San Diego, CA, USA, 1995, p. 28.
[16]
Z. Huang and N. Mamoulis, Heterogeneous information network embedding for meta path based proximity, arXiv preprint arXiv: 1701.05291, 2017.
[17]
H. Ji, C. Shi, and B. Wang, Attention based meta path fusion for heterogeneous information network embedding, in Proc. 15 th Pacific Rim Int. Conf. on Artificial Intelligence, Nanjing, China, 2018, pp. 348–360.
[18]
Z. Liu, Y. Liang, X. Xie, Z. Wang, and Y. Du, FallbackWalk: A random walk based fallback for heterogeneous information network, in Proc. IEEE 6 th Int. Conf. Cloud Computing and Big Data Analytics, Chengdu, China, 2021, pp. 272–280.
[23]
Y. Dong, N. V. Chawla, and A. Swami, metapath2vec: Scalable representation learning for heterogeneous networks, in Proc. 23 rd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Halifax, Canada, 2017, 135–144.
[25]
H. Han, T. Zhao, C. Yang, H. Zhang, Y. Liu, X. Wang, and C. Shi, Openhgnn: an open source toolkit for heterogeneous graph neural network, in Proc. of the 31st ACM International Conference on Information and Knowledge Management, Atlanta, GA, USA, 2022, pp. 3993−3997.