Bibliography¶
Réka Albert and Albert-László Barabási. Statistical mechanics of complex networks. Rev. Mod. Phys., 74:47–97, Jan 2002. doi:10.1103/RevModPhys.74.47.
Renzo Angles, Peter A. Boncz, Josep Llu\'ıs Larriba-Pey, Irini Fundulaki, Thomas Neumann, Orri Erling, Peter Neubauer, Norbert Mart\'ınez-Bazan, Venelin Kotsev, and Ioan Toma. The linked data benchmark council: a graph and RDF industry benchmarking effort. SIGMOD Rec., 43(1):27–31, 2014. doi:10.1145/2627692.2627697.
Guillaume Bagan, Angela Bonifati, Radu Ciucanu, George H. L. Fletcher, Aurélien Lemay, and Nicky Advokaat. Gmark: schema-driven generation of graphs and queries. IEEE Trans. Knowl. Data Eng., 29(4):856–869, 2017. doi:10.1109/TKDE.2016.2633993.
Piero Andrea Bonatti, Stefan Decker, Axel Polleres, and Valentina Presutti. Knowledge graphs: new directions for knowledge representation on the semantic web (dagstuhl seminar 18371). Dagstuhl Reports, 8(9):29–111, 2018. doi:10.4230/DagRep.8.9.29.
Deepayan Chakrabarti, Yiping Zhan, and Christos Faloutsos. R-MAT: A recursive model for graph mining. In Proceedings of the Fourth SIAM International Conference on Data Mining, Lake Buena Vista, Florida, USA, April 22-24, 2004, 442–446. SIAM, 2004. doi:10.1137/1.9781611972740.43.
Nicola De Cao and Thomas Kipf. MolGAN: An implicit generative model for small molecular graphs. ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models, 2018.
Lisa Ehrlinger and Wolfram Wöß. Towards a definition of knowledge graphs. In Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and the 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS'16) co-located with the 12th International Conference on Semantic Systems (SEMANTiCS 2016), Leipzig, Germany, September 12-15, 2016, volume 1695 of CEUR Workshop Proceedings. CEUR-WS.org, 2016.
P ERDdS and A R&wi. On random graphs i. Publ. math. debrecen, 6(290-297):18, 1959.
Zaiwen Feng, Wolfgang Mayer, Keqing He, Selasi Kwashie, Markus Stumptner, Georg Grossmann, Rong Peng, and Wangyu Huang. A schema-driven synthetic knowledge graph generation approach with extended graph differential dependencies (gdd\(^\mbox x\)s). IEEE Access, 9:5609–5639, 2021. doi:10.1109/ACCESS.2020.3048186.
Nikhil Goyal, Harsh Vardhan Jain, and Sayan Ranu. Graphgen: A scalable approach to domain-agnostic labeled graph generation. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 1253–1263. ACM / IW3C2, 2020. doi:10.1145/3366423.3380201.
Thomas R. Gruber. Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum. Comput. Stud., 43(5-6):907–928, 1995. doi:10.1006/ijhc.1995.1081.
Yuanbo Guo, Zhengxiang Pan, and Jeff Heflin. LUBM: A benchmark for OWL knowledge base systems. J. Web Semant., 3(2-3):158–182, 2005. doi:10.1016/j.websem.2005.06.005.
Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann. Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge. Morgan & Claypool Publishers, 2021. doi:10.2200/S01125ED1V01Y202109DSK022.
André Melo and Heiko Paulheim. Synthesizing knowledge graphs for link and type prediction benchmarking. In The Semantic Web - 14th International Conference, ESWC 2017, Portorož, Slovenia, May 28 - June 1, 2017, Proceedings, Part I, volume 10249 of Lecture Notes in Computer Science, 136–151. 2017. doi:10.1007/978-3-319-58068-5\_9.
John Palowitch, Anton Tsitsulin, Brandon Mayer, and Bryan Perozzi. Graphworld: fake graphs bring real insights for gnns. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022, 3691–3701. ACM, 2022. doi:10.1145/3534678.3539203.
Himchan Park and Min-Soo Kim. Trilliong: A trillion-scale synthetic graph generator using a recursive vector model. In Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD Conference 2017, Chicago, IL, USA, May 14-19, 2017, 913–928. ACM, 2017. doi:10.1145/3035918.3064014.
Jan Portisch and Heiko Paulheim. The DLCC node classification benchmark for analyzing knowledge graph embeddings. In The Semantic Web - ISWC 2022 - 21st International Semantic Web Conference, Virtual Event, October 23-27, 2022, Proceedings, volume 13489 of Lecture Notes in Computer Science, 592–609. Springer, 2022. doi:10.1007/978-3-031-19433-7\_34.
Bidisha Samanta, Abir De, Gourhari Jana, Vicenç Gómez, Pratim Kumar Chattaraj, Niloy Ganguly, and Manuel Gomez-Rodriguez. NEVAE: A deep generative model for molecular graphs. J. Mach. Learn. Res., 21:114:1–114:33, 2020.
Martin Simonovsky and Nikos Komodakis. Graphvae: towards generation of small graphs using variational autoencoders. In Artificial Neural Networks and Machine Learning - ICANN 2018 - 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I, volume 11139 of Lecture Notes in Computer Science, 412–422. Springer, 2018. doi:10.1007/978-3-030-01418-6\_41.
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. Graphgan: graph representation learning with generative adversarial nets. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, 2508–2515. AAAI Press, 2018.
Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, and Jure Leskovec. Graphrnn: generating realistic graphs with deep auto-regressive models. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, 5694–5703. PMLR, 2018.