Sun Y, Han J (2012) Mining heterogeneous information networks: a structural analysis approach. ACM SIGKDD Explorations Newsletter
Van Otterlo M (2005) A survey of reinforcement learning in relational domains. Centre for Telematics and Information Technology (CTIT) University of Twente, Tech, Rep
Zheng J, Ma Q, Gu H, Zheng Z (2021) Multi-view denoising graph auto-encoders on heterogeneous information networks for cold-start recommendation. In: Proceedings of the 2021 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 2338–2348
Wan G, Du B, Pan S, Haffari G (2020) Reinforcement learning based meta-path discovery in large-scale heterogeneous information networks. In: Proceedings of the 2020 AAAI Conference on Artificial Intelligence (AAAI), pp. 6094–6101
Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS (2019) Heterogeneous graph attention network. In: Proceedings of the 2019 International Conference on World Wide Web (WWW), pp. 2022–2032
Fu X, Zhang J, Meng Z, King I (2020) MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the 2020 International Conference on World Wide Web (WWW), pp. 2331–2341
Dong Y, Hu Z, Wang K, Sun Y, Tang J (2020) Heterogeneous network representation learning. In: Proceedings of the 2020 International Joint Conferences on Artifical Intelligence (IJCAI), pp. 4861–4867
Dong Y, Chawla N.V, Swami A (2017) metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 2017 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 135–144
Fu T, Lee W, Lei Z (2017) Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM International Conference on Information and Knowledge Management (CIKM), pp. 1797–1806
Shi C, Hu B, Zhao WX, Yu PS (2019) Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionally. In: Proceedings of the 2013 Annual Conference on Neural Information Processing Systems (NIPS), pp. 3111–3119
Le QV, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of the 2014 International Conference on Machine Learning (ICML), pp. 1188–1196
Hussein R, Yang D, Cudré-Mauroux P (2018) Are meta-paths necessary?: Revisiting heterogeneous graph embeddings. In: Proceedings of the 2018 ACM International Conference on Information and Knowledge Management (CIKM), pp. 437–446
Jiang J, Li Z, Ju CJ-, Wang W (2020) MARU: meta-context aware random walks for heterogeneous network representation learning. In: Proceedings of the 2020 ACM International Conference on Information and Knowledge Management (CIKM), pp. 575–584
Zhao J, Wang X, Shi C, Liu Z, Ye Y (2020) Network schema preserving heterogeneous information network embedding. In: Proceedings of the 2020 International Joint Conferences on Artifical Intelligence (IJCAI), pp. 1366–1372
Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 2017 International Conference on Learning Representations (ICLR)
Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. In: Proceedings of the 2018 International Conference on Learning Representations (ICLR)
Schlichtkrull MS, Kipf TN, Bloem P, van den Berg R, Titov I, Welling M (2019) Modeling relational data with graph convolutional networks. In: European Semantic Web Conference (ESWC), pp. 593–607
Zhang C, Song D, Huang C, Swami A, Chawla NV (2019) Heterogeneous graph neural network. In: Proceedings of the 2019 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 793–803
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller MA, Fidjeland A, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D (2015) Human-level control through deep reinforcement learning. Nature 518:529–533 DOI: 10.1038/nature14236
Meng C, Cheng R, Maniu S, Senellart P, Zhang W (2015) Discovering meta-paths in large heterogeneous information networks. In: Proceedings of the 2015 International Conference on World Wide Web (WWW), pp. 754–764
Yang C, Liu M, He F, Zhang X, Peng J, Han J (2018) Similarity modeling on heterogeneous networks via automatic path discovery. In: European Conference on Machine Learning and Knowledge Discovery in Databases (ECMLPKDD), pp. 37–54
Raedt LD (2008) Logical and Relational Learning. Cognitive Technologies
Serafino F, Pio G, Ceci M (2018) Ensemble learning for multi-type classification in heterogeneous networks. IEEE Trans Knowl Data Eng 30(12):2326–2339 DOI: 10.1109/TKDE.2018.2822307
Petkovic M, Ceci M, Kersting K, Dzeroski S (2020) Estimating the importance of relational features by using gradient boosting. In: International Symposium on Foundations of Intelligent Systems (ISMIS). Lecture Notes in Computer Science, vol. 12117, pp. 362–371
Lavrac N, Skrlj B, Robnik-Sikonja M (2020) Propositionalization and embeddings: two sides of the same coin. Mach Learn 109(7):1465–1507 DOI: 10.1007/s10994-020-05890-8
Bruna J, Zaremba W, Szlam A, LeCun Y (2014) Spectral networks and locally connected networks on graphs. In: Proceedings of the 2014 International Conference on Learning Representations (ICLR)
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 2016 Annual Conference on Neural Information Processing Systems (NIPS), pp. 3837–3845
Fan Y, Hou S, Zhang Y, Ye Y, Abdulhayoglu M (2018) Gotcha - sly malware!: Scorpion A metagraph2vec based malware detection system. In: Proceedings of the 2018 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 253–262
Hu Z, Dong Y, Wang K, Sun Y (2020) Heterogeneous graph transformer. In: Proceedings of the 2020 International Conference on World Wide Web (WWW), pp. 2704–2710
Yun S, Jeong M, Kim R, Kang J, Kim HJ (2019) Graph transformer networks. In: Proceedings of the 2019 Annual Conference on Neural Information Processing Systems (NeurIPS), pp. 11960–11970
Tanon TP, Stepanova D, Razniewski S, Mirza P, Weikum G (2018) Completeness-aware rule learning from knowledge graphs. In: Proceedings of the 2018 International Joint Conferences on Artifical Intelligence (IJCAI), pp. 5339–5343
Ahmadi N, Huynh V, Meduri VV, Ortona S, Papotti P (2020) Mining expressive rules in knowledge graphs. ACM Journal of Data and Information Quality
Sutton RS, Barto AG (1998) Reinforcement learning: An introduction. IEEE Transactions on Neural Networks and Learning Systems 9(5):1054–1054 DOI: 10.1109/TNN.1998.712192
Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA (2017) Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine
Haykin S (1999) Neural networks: A comprehensive foundation. Knowledge Engineering Review
Hou Y, Liu L, Wei Q, Xu X, Chen C (2017) A novel DDPG method with prioritized experience replay. In: Proceedings of the 2017 International Conference on Systems Man and Cybernetics (SMC), pp. 316–321
Fan J, Wang Z, Xie Y, Yang Z (2020) A theoretical analysis of deep q-learning. In: Proceedings of the 2020 Annual Conference on Learning for Dynamics and Control (L4DC). Proceedings of Machine Learning Research, vol. 120, pp. 486–489
Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 2015 International Conference on World Wide Web (WWW), pp. 1067–1077
Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 2014 ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 701–710
Shang J, Qu M, Liu J, Kaplan LM, Han J, Peng J (2016) Meta-path guided embedding for similarity search in large-scale heterogeneous information networks. CoRR abs/1610.09769