A curated list of amazingly awesome things regarding Graph Structural Learning.
The learning of graph structure is a fundamental problem of a wide range of applications. Here, we collect and summarize the toolboxes, datasets, surveys, related works and other useful open-source resources which can be modelled as a graph structural learning problem. To our best knowledge, we will cover different types of graphs, and their further applications.
- Directed acyclic graph (e.g., Causal graph or Bayesian network, Neural network)
- Bayesian Network Learning
- Undirected graph
- Knowledge Graph Complement
- Graph Neural Network Architecture Search
| Name | Description | Code |
|---|---|---|
| CausalDiscoveryToolbox | Causal Discovery could be modelled as a dynamic learning problem of Directed acyclic graph(DAG). This is a pytorch-based toolbox, including constraint-based methods (e..g, PC), score-based methods(e.g., GES, GS, Pairwise) as well as useful implementation of performance measurement such as PR,SHD,SID | https://github.com/FenTechSolutions/CausalDiscoveryToolbox |
| Dataset Name | Nodes | Arcs | Average Degree | Max In-degree | Free parameters | description | |
|---|---|---|---|---|---|---|---|
| Asia | 8 | 8 | 2 | 2 | 18 | 2.25 | prior knowledge |
| Alarm | 37 | 46 | 2.49 | 4 | 509 | 13.75676 | prior knowledge |
| Formed | 88 | 138 | 3.14 | 6 | 912 | 10.36364 | realdata |
| Sports | 9 | 15 | 3.33 | 2 | 1059 | 117.6667 | realdata |
| Property | 27 | 31 | 2.3 | 3 | 3056 | 113.1852 | defined rule + regulatig protocol |
| Pathfinder | 109 | 195 | 3.58 | 5 | 71890 | 659.5413 | prior knowledge |
Related Papers in 2021 CCFA Conference
- M. J. Vowels, N. C. Camgoz, and R. Bowden, ‘D’ya like DAGs? A Survey on Structure Learning and Causal Discovery’, arXiv:2103.02582 [cs, stat], Mar. 2021.
- M. Scanagatta, A. Salmerón, and F. Stella, ‘A survey on Bayesian network structure learning from data’, Prog Artif Intell, vol. 8, no. 4, pp. 425–439, Dec. 2019, doi: 10.1007/s13748-019-00194-y.
- C. Glymour, K. Zhang, and P. Spirtes, ‘Review of Causal Discovery Methods Based on Graphical Models’, Front. Genet., vol. 10, p. 524, Jun. 2019, doi: 10.3389/fgene.2019.00524.
- B. Schölkopf et al., "Toward Causal Representation Learning," in Proceedings of the IEEE, vol. 109, no. 5, pp. 612-634, May 2021, doi: 10.1109/JPROC.2021.3058954.
[1] X. Zheng, B. Aragam, P. Ravikumar, and E. P. Xing, ‘DAGs with NO TEARS: Continuous Optimization for Structure Learning’, in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, 2018, pp. 9492–9503.
[2] Lachapelle, S., Brouillard, P., Deleu, T. & Lacoste-Julien, S. Gradient-Based Neural DAG Learning. ICLR, 2020, Addis Ababa, Ethiopia, April 26-30, 2020.
[3] Cussens et al. Polyhedral aspects of score equivalence in Bayesian network structure learning. Mathematical Programming, 164(1-2), 285–324, 2017.
[4] Y. W. Park and D. Klabjan, ‘Bayesian Network Learning via Topological Order’, J. Mach. Learn. Res., vol. 18, p. 99:1-99:32, 2017.
[5] Chickering et al. Optimal structure identification with greedy search. JMLR, 3, pp. 507–554, 2003.
[6] Ramsey et al. A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images. International Journal of Data Science and Analytics, pp. 1–9. 2016
[7] J. Xiang and S. Kim, ‘A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables’, in Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, 2013, pp. 2418–2426.
[8] Y. Yu, J. Chen, T. Gao, and M. Yu, ‘DAG-GNN: DAG Structure Learning with Graph Neural Networks, in Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, 2019, vol. 97, pp. 7154–7163.
[9]S. Lachapelle, P. Brouillard, T. Deleu, and S. Lacoste-Julien, ‘Gradient-Based Neural DAG Learning’, 2020.
[10] H. Liu, K. Simonyan, and Y. Yang, ‘DARTS: Differentiable Architecture Search’, 2019.
[11] R. Zhu et al., ‘Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications’, arXiv:2012.03540 [cs], Dec. 2020.
[1] J. You et al, “Graph Structure of Neural Networks,” ICML, Jul. 2020
[2] lskene, Thomas, et al. Neural architecture search: A survey, pp. 1-21. JMLR, 2019.
[3] Hanxiao Liu, et al. Hierarchical Representations for Efficient Architecture Search, ICLR, 2018.
[4] Sirui Xie, Stochastic Neural Architecture Search, ICLR, 2019
[5] Hanxiao Liu, et al. DARTS: Differentiable architecture search, ICLR, 2019.
[6] Senior, A.W., Evans, R., Jumper, J. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, pp. 706–710, 2020.
[7] Translating embeddings for modeling multi-relational data NIPS2013
[8] Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach, IJCAI, 2017