{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:51:55Z","timestamp":1765504315296,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":71,"publisher":"ACM","funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["112-2221-E-007 -088 -MY3, 113-2628-E-007 -012 -MY3,"],"award-info":[{"award-number":["112-2221-E-007 -088 -MY3, 113-2628-E-007 -012 -MY3,"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761139","type":"proceedings-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:29:28Z","timestamp":1762561768000},"page":"877-887","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Model-Agonistic Iterative Graph Diversification for Improving Learning to Solve Graph Optimization Problems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8080-3951","authenticated-orcid":false,"given":"Bay-Yuan","family":"Hsu","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5966-8987","authenticated-orcid":false,"given":"Chia-Hsun","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0377-7945","authenticated-orcid":false,"given":"Chih-Ya","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Statistical mechanics of complex networks. Rev. Mod. Phys","author":"Albert Reka","year":"2002","unstructured":"Reka Albert and Albert-Laszlo Barabasi. 2002. Statistical mechanics of complex networks. Rev. Mod. Phys (2002), 47-97."},{"key":"e_1_3_2_1_2_1","unstructured":"Anonymized. 2025. Anonymized reproducibility materials and online full version (codes models and documents). In https:\/\/tinyurl.com\/4tkvc5kj."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/0196-6774(81)90020-1"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_2_1_5_1","volume-title":"Efficient and Scalable Graph Generation through Iterative Local Expansion. arXiv preprint arXiv:2312.11529","author":"Bergmeister Andreas","year":"2023","unstructured":"Andreas Bergmeister, Karolis Martinkus, Nathana\u00ebl Perraudin, and Roger Wattenhofer. 2023. Efficient and Scalable Graph Generation through Iterative Local Expansion. arXiv preprint arXiv:2312.11529 (2023)."},{"key":"e_1_3_2_1_6_1","first-page":"31226","article-title":"Learning generalizable models for vehicle routing problems via knowledge distillation","volume":"35","author":"Bi Jieyi","year":"2022","unstructured":"Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, and Yeow Meng Chee. 2022. Learning generalizable models for vehicle routing problems via knowledge distillation. Advances in Neural Information Processing Systems, Vol. 35 (2022), 31226-31238.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3152179"},{"key":"e_1_3_2_1_8_1","volume-title":"Yu Zhang","author":"Chen Mingzhe","year":"2020","unstructured":"Mingzhe Chen, Jiawei Zhang, and Philip S. Yu Zhang. 2020. CONE-Align: Consistent Network Alignment with Node Embeddings. arXiv preprint arXiv:2005.04725 (2020)."},{"key":"e_1_3_2_1_9_1","volume-title":"Efficient and degree-guided graph generation via discrete diffusion modeling. arXiv preprint arXiv:2305.04111","author":"Chen Xiaohui","year":"2023","unstructured":"Xiaohui Chen, Jiaxing He, Xu Han, and Li-Ping Liu. 2023. Efficient and degree-guided graph generation via discrete diffusion modeling. arXiv preprint arXiv:2305.04111 (2023)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2875911"},{"key":"e_1_3_2_1_11_1","first-page":"17","article-title":"On the evolution of random graphs","author":"Erdos P.","year":"1960","unstructured":"P. Erdos and A Renyi. 1960. On the evolution of random graphs. In Publication of the Mathematical Institute of the Academy of Sciences. 17-61.","journal-title":"Publication of the Mathematical Institute of the Academy of Sciences."},{"key":"e_1_3_2_1_12_1","volume-title":"Graph Random Neural Network for Semi-Supervised Learning on Graphs. Advances in Neural Information Processing Systems","author":"Feng Wenzheng","year":"2020","unstructured":"Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. 2020. Graph Random Neural Network for Semi-Supervised Learning on Graphs. Advances in Neural Information Processing Systems (2020)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25547"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/S14-1010"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/195058.195221"},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning. PMLR, 8230-8248","author":"Han Xiaotian","year":"2022","unstructured":"Xiaotian Han, Zhimeng Jiang, Ninghao Liu, and Xia Hu. 2022. G-mixup: Graph data augmentation for graph classification. In International Conference on Machine Learning. PMLR, 8230-8248."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.65.026107"},{"key":"e_1_3_2_1_18_1","volume-title":"Hoos and Thomas St\u00fctzle","author":"Holger","year":"2000","unstructured":"Holger H. Hoos and Thomas St\u00fctzle. 2000. SATLIB: an online resource for research on SAT. IOS Press, 283-292."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3446169"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00030"},{"key":"e_1_3_2_1_21_1","volume-title":"Towards the Generalization of Contrastive Self-Supervised Learning. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=XDJwuEYHhme","author":"Huang Weiran","year":"2023","unstructured":"Weiran Huang, Mingyang Yi, Xuyang Zhao, and Zihao Jiang. 2023. Towards the Generalization of Contrastive Self-Supervised Learning. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=XDJwuEYHhme"},{"key":"e_1_3_2_1_22_1","first-page":"113","article-title":"Application of minimum vertex cover for keyword-based text summarization process","volume":"13","author":"Islam Atowar-Ul","year":"2017","unstructured":"Atowar-Ul Islam and Bichitra Kalita. 2017. Application of minimum vertex cover for keyword-based text summarization process. International Journal of Computational Intelligence Research, Vol. 13 (2017), 113-125.","journal-title":"International Journal of Computational Intelligence Research"},{"key":"e_1_3_2_1_23_1","volume-title":"International conference on machine learning. PMLR, 2323-2332","author":"Jin Wengong","year":"2018","unstructured":"Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018. Junction tree variational autoencoder for molecular graph generation. In International conference on machine learning. PMLR, 2323-2332."},{"key":"e_1_3_2_1_24_1","volume-title":"Graph Generation with Diffusion Mixture. arXiv preprint arXiv:2302.03596","author":"Jo Jaehyeong","year":"2023","unstructured":"Jaehyeong Jo, Dongki Kim, and Sung Ju Hwang. 2023. Graph Generation with Diffusion Mixture. arXiv preprint arXiv:2302.03596 (2023)."},{"key":"e_1_3_2_1_25_1","first-page":"6348","article-title":"Learning combinatorial optimization algorithms over graphs. In Advances in Neural Information Processing Systems. Curran Associates","author":"Khalil Elias","year":"2017","unstructured":"Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song. 2017. Learning combinatorial optimization algorithms over graphs. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 6348-6358.","journal-title":"Inc."},{"key":"e_1_3_2_1_26_1","first-page":"10418","article-title":"Learning collaborative policies to solve NP-hard routing problems","volume":"34","author":"Kim Minsu","year":"2021","unstructured":"Minsu Kim, Jinkyoo Park, et al., 2021. Learning collaborative policies to solve NP-hard routing problems. Advances in Neural Information Processing Systems, Vol. 34 (2021), 10418-10430.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_27_1","first-page":"1936","article-title":"Sym-nco: Leveraging symmetricity for neural combinatorial optimization","volume":"35","author":"Kim Minsu","year":"2022","unstructured":"Minsu Kim, Junyoung Park, and Jinkyoo Park. 2022. Sym-nco: Leveraging symmetricity for neural combinatorial optimization. Advances in Neural Information Processing Systems, Vol. 35 (2022), 1936-1949.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_28_1","volume-title":"Autoregressive diffusion model for graph generation","author":"Kong Lingkai","year":"2023","unstructured":"Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B Aditya Prakash, and Chao Zhang. ''2023''. Autoregressive diffusion model for graph generation, 2023. In URL https:\/\/openreview. net\/forum."},{"key":"e_1_3_2_1_29_1","volume-title":"International Conference on Learning Representations.","author":"Kool Wouter","year":"2019","unstructured":"Wouter Kool, Herke van Hoof, and Max Welling. 2019. Attention, Learn to Solve Routing Problems!. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_30_1","first-page":"539","article-title":"Combinatorial optimization with graph convolutional networks and guided tree search. In Advances in Neural Information Processing Systems 31. Curran Associates","author":"Li Zhuwen","year":"2018","unstructured":"Zhuwen Li, Qifeng Chen, and Vladlen Koltun. 2018. Combinatorial optimization with graph convolutional networks and guided tree search. In Advances in Neural Information Processing Systems 31. Curran Associates, Inc., 539-548.","journal-title":"Inc."},{"key":"e_1_3_2_1_31_1","volume-title":"Joint learning of consistent and complementary representations for multi-view clustering. arXiv preprint arXiv:2008.10208","author":"Liang Jun","year":"2020","unstructured":"Jun Liang, Chen Liu, Zhenghua Zhang, Xiangnan He, and Yitong Li. 2020. Joint learning of consistent and complementary representations for multi-view clustering. arXiv preprint arXiv:2008.10208 (2020)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3105232"},{"key":"e_1_3_2_1_33_1","volume-title":"International Conference on Machine Learning. PMLR, 14054-14072","author":"Liu Songtao","year":"2022","unstructured":"Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, and Dinghao Wu. 2022. Local augmentation for graph neural networks. In International Conference on Machine Learning. PMLR, 14054-14072."},{"key":"e_1_3_2_1_34_1","volume-title":"Neural combinatorial optimization with heavy decoder: Toward large scale generalization. Advances in Neural Information Processing Systems","author":"Luo Fu","year":"2023","unstructured":"Fu Luo, Xi Lin, Fei Liu, Qingfu Zhang, and Zhenkun Wang. 2023. Neural combinatorial optimization with heavy decoder: Toward large scale generalization. Advances in Neural Information Processing Systems (2023)."},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Machine Learning. PMLR, 15159-15179","author":"Martinkus Karolis","year":"2022","unstructured":"Karolis Martinkus, Andreas Loukas, Nathana\u00ebl Perraudin, and Roger Wattenhofer. 2022. Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators. In International Conference on Machine Learning. PMLR, 15159-15179."},{"key":"e_1_3_2_1_36_1","first-page":"9839","article-title":"Reinforcement Learning for Solving the Vehicle Routing Problem. In Advances in Neural Information Processing Systems 31. Curran Associates","author":"Nazari MohammadReza","year":"2018","unstructured":"MohammadReza Nazari, Afshin Oroojlooy, Lawrence Snyder, and Martin Takac. 2018. Reinforcement Learning for Solving the Vehicle Routing Problem. In Advances in Neural Information Processing Systems 31. Curran Associates, Inc., 9839-9849.","journal-title":"Inc."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014731"},{"volume-title":"2015 IEEE International Congress on Big Data. 467-474","author":"Puthal D.","key":"e_1_3_2_1_38_1","unstructured":"D. Puthal, S. Nepal, C. Paris, R. Ranjan, and J. Chen. 2015. Efficient algorithms for social network coverage and reach. In 2015 IEEE International Congress on Big Data. 467-474."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci025605o"},{"key":"e_1_3_2_1_40_1","volume-title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In International Conference on Learning Representations.","author":"Rong Yu","year":"2019","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1006\/jmbi.1998.1689"},{"volume-title":"International Conference on Learning Representations.","author":"Selsam Daniel","key":"e_1_3_2_1_42_1","unstructured":"Daniel Selsam, Matthew Lamm, Benedikt B\u00fcnz, Percy Liang, Leonardo de Moura, and David L. Dill. 2019. Learning a SAT solver from single-bit supervision. In International Conference on Learning Representations."},{"volume-title":"Project: Plug-And-Play Controllable Graph Generation. In Forty-first International Conference on Machine Learning.","author":"Sharma Kartik","key":"e_1_3_2_1_43_1","unstructured":"Kartik Sharma, Srijan Kumar, and Rakshit Trivedi. ''2024''. Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.14778\/3574245.3574249"},{"key":"e_1_3_2_1_45_1","first-page":"7783","article-title":"Evaluating graph generative models with contrastively learned features","volume":"35","author":"Shirzad Hamed","year":"2022","unstructured":"Hamed Shirzad, Kaveh Hassani, and Danica J Sutherland. 2022. Evaluating graph generative models with contrastively learned features. Advances in Neural Information Processing Systems, Vol. 35 (2022), 7783-7795.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_46_1","volume-title":"Difusco: Graph-based diffusion solvers for combinatorial optimization. arXiv preprint arXiv:2302.08224","author":"Sun Zhiqing","year":"2023","unstructured":"Zhiqing Sun and Yiming Yang. 2023. Difusco: Graph-based diffusion solvers for combinatorial optimization. arXiv preprint arXiv:2302.08224 (2023)."},{"key":"e_1_3_2_1_47_1","first-page":"15920","article-title":"Adversarial graph augmentation to improve graph contrastive learning","volume":"34","author":"Suresh Susheel","year":"2021","unstructured":"Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021. Adversarial graph augmentation to improve graph contrastive learning. Advances in Neural Information Processing Systems, Vol. 34 (2021), 15920-15933.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_48_1","unstructured":"Cormen Thomas H E Charles Rivest Ronald L Stein Clifford et al. 2009. Introduction to Algorithms Third Edition."},{"key":"e_1_3_2_1_49_1","volume-title":"Digress: Discrete denoising diffusion for graph generation. arXiv preprint arXiv:2209.14734","author":"Vignac Clement","year":"2022","unstructured":"Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard. 2022. Digress: Discrete denoising diffusion for graph generation. arXiv preprint arXiv:2209.14734 (2022)."},{"key":"e_1_3_2_1_50_1","first-page":"2692","article-title":"Pointer networks. In Advances in Neural Information Processing Systems 28. Curran Associates","author":"Vinyals Oriol","year":"2015","unstructured":"Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. In Advances in Neural Information Processing Systems 28. Curran Associates, Inc., 2692-2700.","journal-title":"Inc."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11872"},{"key":"e_1_3_2_1_52_1","first-page":"31444","article-title":"Unsupervised learning for combinatorial optimization with principled objective relaxation","volume":"35","author":"Wang Haoyu Peter","year":"2022","unstructured":"Haoyu Peter Wang, Nan Wu, Hang Yang, Cong Hao, and Pan Li. 2022b. Unsupervised learning for combinatorial optimization with principled objective relaxation. Advances in Neural Information Processing Systems, Vol. 35 (2022), 31444-31458.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_53_1","volume-title":"Consistent Multiple Graph Embedding Clustering via Multi-Graph Autoencoder. arXiv preprint arXiv:2105.04880","author":"Wang Lin","year":"2021","unstructured":"Lin Wang, Yunpeng Ma, Jun Sun, and Yucheng Liang. 2021b. Consistent Multiple Graph Embedding Clustering via Multi-Graph Autoencoder. arXiv preprint arXiv:2105.04880 (2021)."},{"key":"e_1_3_2_1_54_1","first-page":"21453","article-title":"A bi-level framework for learning to solve combinatorial optimization on graphs","volume":"34","author":"Wang Runzhong","year":"2021","unstructured":"Runzhong Wang, Zhigang Hua, Gan Liu, Jiayi Zhang, Junchi Yan, Feng Qi, Shuang Yang, Jun Zhou, and Xiaokang Yang. 2021a. A bi-level framework for learning to solve combinatorial optimization on graphs. Advances in Neural Information Processing Systems, Vol. 34 (2021), 21453-21466.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_55_1","first-page":"4555","article-title":"A Survey on Curriculum Learning","volume":"44","author":"Wang Xin","year":"2022","unstructured":"Xin Wang, Yudong Chen, and Wenwu Zhu. 2022a. A Survey on Curriculum Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 9 (2022), 4555-4576.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449796"},{"key":"e_1_3_2_1_57_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Xiao Teng","year":"2024","unstructured":"Teng Xiao, Huaisheng Zhu, Zhengyu Chen, and Suhang Wang. 2024. Simple and asymmetric graph contrastive learning without augmentations. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_58_1","first-page":"2280","article-title":"Consistency-driven alternating optimization for multi-graph matching","volume":"23","author":"Yan Junchi","year":"2014","unstructured":"Junchi Yan, Hongyuan Zha, Xiaokang Yang, and Wen Chu. 2014. Consistency-driven alternating optimization for multi-graph matching. IEEE Transactions on Image Processing, Vol. 23, 5 (2014), 2280-2291.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.207"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539437"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45041.2023.10279825"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20871"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512175"},{"key":"e_1_3_2_1_64_1","volume-title":"Graph generative model for benchmarking graph neural networks. arXiv preprint arXiv:2207.04396","author":"Yoon Minji","year":"2022","unstructured":"Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, and Ruslan Salakhutdinov. 2022. Graph generative model for benchmarking graph neural networks. arXiv preprint arXiv:2207.04396 (2022)."},{"key":"e_1_3_2_1_65_1","volume-title":"International Conference on Machine Learning","author":"You Yuning","year":"2021","unstructured":"Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. 2021. Graph contrastive learning automated. International Conference on Machine Learning (2021)."},{"key":"e_1_3_2_1_66_1","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in Neural Information Processing Systems, Vol. 33 (2020), 5812-5823.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26336"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17315"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.13285"},{"key":"e_1_3_2_1_70_1","volume-title":"Molhf: A hierarchical normalizing flow for molecular graph generation. arXiv preprint arXiv:2305.08457","author":"Zhu Yiheng","year":"2023","unstructured":"Yiheng Zhu, Zhenqiu Ouyang, Ben Liao, Jialu Wu, Yixuan Wu, Chang-Yu Hsieh, Tingjun Hou, and Jian Wu. 2023. Molhf: A hierarchical normalizing flow for molecular graph generation. arXiv preprint arXiv:2305.08457 (2023)."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Seoul Republic of Korea","acronym":"CIKM '25"},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:50:41Z","timestamp":1765504241000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":71,"alternative-id":["10.1145\/3746252.3761139","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761139","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}