Bakalárska práca

Knihy

• Wu, L., Cui, P., Pei, J., & Zhao, L. (2022). Graph Neural Networks: Foundations, Frontiers, and Applications. Springer Nature Singapore. DOI: 10.1007/978-981-16-6054-2
• Diestel, R. (2017). Graph Theory (Graduate Texts in Mathematics, Vol. 173). Springer Berlin Heidelberg. DOI: 10.1007/978-3-662-53622-3
• West, D.B. (2001). Introduction to Graph Theory. Prentice Hall.
• Biggs, N. (1993). Algebraic Graph Theory (Cambridge Mathematical Library). Cambridge University Press.
• Trudeau, R.J. (1993). Introduction to Graph Theory. Dover Publications.
• Bollobás, B. (1985). Random Graphs (Cambridge Studies in Advanced Mathematics). Academic Press.
• Bondy, J.A., & Murty, U.S.R. (2008). Graph Theory (Graduate Texts in Mathematics, Vol. 244, 2nd ed.). Springer.
• Irniger, C.A.M. (2005). Graph Matching: Filtering Databases of Graphs Using Machine Learning Techniques. AKA.
• Chartrand, G., Jordon, H., Vatter, V., & Zhang, P. (2024). Graphs and Digraphs (Textbooks in Mathematics Series, 7th ed.). CRC Press LLC.

Články a vedecké práce

• Grohe, M. (2022). The Logic of Graph Neural Networks. arXiv. arXiv:2104.14624
• Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57-81. DOI: 10.1016/j.aiopen.2021.01.001
• Shervashidze, N., Schweitzer, P., Leeuwen, E.J.V., Mehlhorn, K., & Borgwardt, K.M. (2011). Weisfeiler-Lehman Graph Kernels. Journal of Machine Learning Research, 12(77), 2539-2561. Link
• Morris, C., Ritzert, M., Fey, M., Hamilton, W.L., Lenssen, J.E., Rattan, G., & Grohe, M. (2019). Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 4602-4609. DOI: 10.1609/aaai.v33i01.33014602
• Huang, N.T., & Villar, S. (2021). A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing, 8533-8537. DOI: 10.1109/ICASSP39728.2021.9413523
• Weisfeiler, B., & Lehman, A.A. (1968). A Reduction of a Graph to a Canonical Form and an Algebra Arising During This Reduction. Nauchno-Technicheskaya Informatsia, 2(9), 12-16.
• Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How Powerful are Graph Neural Networks? arXiv. arXiv:1810.00826
• Jajcay, R., Jajcayova, T., Szakács, N., & Szendrei, M.B. (2020). Inverse monoids of partial graph automorphisms. arXiv. arXiv:1809.04422
• Paolino, R., Maskey, S., Welke, P., & Kutyniok, G. (2024). Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning. arXiv. arXiv:2403.13749
• Coolsaet, K., D'hondt, S., & Goedgebeur, J. (2023). House of Graphs 2.0: A database of interesting graphs and more. Discrete Applied Mathematics, 325, 97-107. https://houseofgraphs.org
• McKay, B.D., & Piperno, A. (2014). Practical graph isomorphism, II. Journal of Symbolic Computation, 60, 94-112.
• Kimble, R., Schwenk, A., & Stockmeyer, P. (1981). Pseudosimilar vertices in a graph. Journal of Graph Theory, 5, 171-181.
• Godsil, C.D., & Kocay, W.L. (1982). Constructing graphs with pairs of pseudo-similar vertices. Journal of Combinatorial Theory, Series B, 32(4), 392-399.
• Baird, H.S., & Cho, Y.E. (1975). An artwork design verification system. Proceedings of the 12th Design Automation Conference, 414-420.

Software nástroje a knižnice

• Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., & Dahl, G.E. (2017). Neural Message Passing for Quantum Chemistry. arXiv. arXiv:1704.01212
• You, J., Gomes-Selman, J., Ying, R., & Leskovec, J. (2021). Identity-aware Graph Neural Networks. arXiv. arXiv:2101.10320
• Fey, M., Sunil, J., Nitta, A., Puri, R., Shah, M., Stojanovič, B., Bendias, R., Barghi, A., Kocijan, V., Zhang, Z., He, X., Lenssen, J.E., & Leskovec, J. (2025). PyG 2.0: Scalable Learning on Real World Graphs. arXiv. arXiv:2507.16991
• Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
• Hagberg, A.A., Schult, D.A., & Swart, P.J. (2008). Exploring Network Structure, Dynamics, and Function using NetworkX. Proceedings of the 7th Python in Science Conference, 11–15.
• Loshchilov, I., & Hutter, F. (2019). Decoupled Weight Decay Regularization. arXiv. arXiv:1711.05101
• Kingma, D.P., & Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv. arXiv:1412.6980