[14] John Lafferty, Andrew McCallum, and Fernando CN Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. 2001. 1, 4
[15] Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data with label propagation. 2002. 1
[16] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 2020. 2, 4, 5, 14
[17] BE Rhoades. Some fixed point iteration procedures. International Journal of Mathematics and Mathematical Sciences, 14(1):1-16, 1991. 2, 5
[18] Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, and Jong Chul Ye. Improving diffusion models for inverse problems using manifold constraints. arXiv preprint arXiv:2206.00941, 2022. 2, 3, 4, 5, 15
[19] Boris Weisfeiler and Andrei Leman. The reduction of a graph to canonical form and the algebra which appears therein. nti, Series, 2(9):12-16, 1968. 2, 3, 6, 16
[20] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018. 2, 3, 6, 21
[21] Petar Velicković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph attention networks. In International Conference on Learning Representations, 2018. 2, 7, 8
[22] Zhilin Yang, William Cohen, and Ruslan Salakhudinov. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning, pages 40-48. PMLR, 2016. 2, 7, 23
[23] Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Gunnemann. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868, 2018. 2, 7, 23
[24] Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, and Liudmila Prokhorenkova. A critical look at the evaluation of gnns under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations. 2, 7, 8, 23, 25
[25] Marinka Zitnik and Jure Leskovec. Predicting multicellular function through multi-layer tissue networks. Bioinformatics, 33(14):i190-i198, 2017. 2, 8, 23
[26] Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch. Learning iterative reasoning through energy minimization. In International Conference on Machine Learning, pages 5570-5582. PMLR, 2022. 2, 3, 8, 9, 22, 24, 26
[27] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017. 3
[28] Ben Chamberlain, James Rowbottom, Maria I Gorinova, Michael Bronstein, Stefan Webb, and Emanuele Rossi. Grand: Graph neural diffusion. In International Conference on Machine Learning, pages 1407-1418. PMLR, 2021. 3
[29] David Belanger and Andrew McCallum. Structured prediction energy networks. In International Conference on Machine Learning, pages 983-992. PMLR, 2016. 3
[30] Colin Graber, Ofer Meshi, and Alexander Schwing. Deep structured prediction with nonlinear output transformations. Advances in Neural Information Processing Systems, 31, 2018.
[31] Victor Garcia Satorras, Zeynep Akata, and Max Welling. Combining generative and discriminative models for hybrid inference. Advances in Neural Information Processing Systems, 32, 2019. 3
[32] Julian Besag. Statistical analysis of non-lattice data. Journal of the Royal Statistical Society: Series D (The Statistician), 24(3):179-195, 1975. 3, 5
[33] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021. 3
[34] Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B Hashimoto. Diffusion-LM improves controllable text generation. arXiv preprint arXiv:2205.14217, 2022. 3