{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T08:19:14Z","timestamp":1783153154279,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":59,"publisher":"ACM","funder":[{"name":"The Key R&D Project of Jilin Province","award":["20240304200SF"],"award-info":[{"award-number":["20240304200SF"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3774904.3792497","type":"proceedings-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:38:33Z","timestamp":1777293513000},"page":"4746-4757","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["TGSBM: Transformer-Guided Stochastic Block Model for Link Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6980-8062","authenticated-orcid":false,"given":"Zhejian","family":"Yang","sequence":"first","affiliation":[{"name":"Jilin University, Changchun, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6149-3214","authenticated-orcid":false,"given":"Songwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Jilin University, Changchun, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2190-958X","authenticated-orcid":false,"given":"Zilin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Jilin University, Changchun, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7835-9556","authenticated-orcid":false,"given":"Hechang","family":"Chen","sequence":"additional","affiliation":[{"name":"Jilin University, Changchun, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"1","article-title":"Community detection and stochastic block models: recent developments","volume":"18","author":"Abbe Emmanuel","year":"2018","unstructured":"Emmanuel Abbe. 2018. Community detection and stochastic block models: recent developments. Journal of Machine Learning Research, Vol. 18, 177 (2018), 1-86.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_2_1","volume-title":"Friends and neighbors on the web. Social networks","author":"Adamic Lada A","year":"2003","unstructured":"Lada A Adamic and Eytan Adar. 2003. Friends and neighbors on the web. Social networks, Vol. 25, 3 (2003), 211-230."},{"key":"e_1_3_2_1_3_1","volume-title":"The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems","author":"Brin Sergey","year":"1998","unstructured":"Sergey Brin and Lawrence Page. 1998. The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, Vol. 30, 1-7 (1998), 107-117."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asy070"},{"key":"e_1_3_2_1_5_1","volume-title":"Graph Neural Networks for Link Prediction with Subgraph Sketching. In International Conference on Learning Representations.","author":"Chamberlain Benjamin Paul","year":"2021","unstructured":"Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Yannick Hammerla, Michael M Bronstein, and Max Hansmire. 2021. Graph Neural Networks for Link Prediction with Subgraph Sketching. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_6_1","volume-title":"NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs. In International Conference on Learning Representations.","author":"Chen Jinsong","year":"2023","unstructured":"Jinsong Chen, Kaiyuan Gao, Gaichao Li, and Kun He. 2023. NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.812"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/780542.780646"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOS1354"},{"key":"e_1_3_2_1_10_1","volume-title":"International conference on machine learning. Pmlr, 1263-1272","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International conference on machine learning. Pmlr, 1263-1272."},{"key":"e_1_3_2_1_11_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_12_1","volume-title":"Kathryn Blackmond Laskey, and Samuel Leinhardt","author":"Holland Paul W","year":"1983","unstructured":"Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt. 1983. Stochastic blockmodels: First steps. Social networks, Vol. 5, 2 (1983), 109-137."},{"key":"e_1_3_2_1_13_1","volume-title":"Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, Vol. 33 (2020), 22118-22133."},{"key":"e_1_3_2_1_14_1","volume-title":"Categorical Reparameterization with Gumbel-Softmax. In International Conference on Learning Representations.","author":"Jang Eric","year":"2017","unstructured":"Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289026"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1060"},{"key":"e_1_3_2_1_17_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_1_18_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf TN","year":"2016","unstructured":"TN Kipf. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_19_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_1_20_1","first-page":"21618","article-title":"Rethinking graph transformers with spectral attention","volume":"34","author":"Kreuzer Devin","year":"2021","unstructured":"Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent L\u00e9tourneau, and Prudencio Tossou. 2021. Rethinking graph transformers with spectral attention. Advances in Neural Information Processing Systems, Vol. 34 (2021), 21618-21629.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOAS382"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1214\/14-AOS1274"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0169"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110068"},{"key":"e_1_3_2_1_25_1","volume-title":"The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. In International Conference on Learning Representations.","author":"Maddison Chris J","year":"2017","unstructured":"Chris J Maddison, Andriy Mnih, and Yee Whye Teh. 2017. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_26_1","volume-title":"International Conference on Learning Representations.","author":"Mao Haitao","year":"2024","unstructured":"Haitao Mao, Juanhui Li, Harry Shomer, Bingheng Li, Wenqi Fan, Yao Ma, Tong Zhao, Neil Shah, and Jiliang Tang. 2024. Revisiting Link Prediction: a data perspective. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_27_1","volume-title":"International Conference on Machine Learning. PMLR, 4466-4474","author":"Mehta Nikhil","year":"2019","unstructured":"Nikhil Mehta, Lawrence Carin Duke, and Piyush Rai. 2019. Stochastic blockmodels meet graph neural networks. In International Conference on Machine Learning. PMLR, 4466-4474."},{"key":"e_1_3_2_1_28_1","volume-title":"Nonparametric latent feature models for link prediction. Advances in neural information processing systems","author":"Miller Kurt","year":"2009","unstructured":"Kurt Miller, Michael Jordan, and Thomas Griffiths. 2009. Nonparametric latent feature models for link prediction. Advances in neural information processing systems, Vol. 22 (2009)."},{"key":"e_1_3_2_1_29_1","volume-title":"Structural transition in social networks: The role of homophily. Scientific reports","author":"Murase Yohsuke","year":"2019","unstructured":"Yohsuke Murase, Hang-Hyun Jo, J\u00e1nos T\u00f6r\u00f6k, J\u00e1nos Kert\u00e9sz, and Kimmo Kaski. 2019. Structural transition in social networks: The role of homophily. Scientific reports, Vol. 9, 1 (2019), 4310."},{"key":"e_1_3_2_1_30_1","volume-title":"Stick-Breaking Variational Autoencoders. In International Conference on Learning Representations.","author":"Nalisnick Eric","year":"2017","unstructured":"Eric Nalisnick and Padhraic Smyth. 2017. Stick-Breaking Variational Autoencoders. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_31_1","volume-title":"Clustering and preferential attachment in growing networks. Physical review E","author":"Newman Mark EJ","year":"2001","unstructured":"Mark EJ Newman. 2001. Clustering and preferential attachment in growing networks. Physical review E, Vol. 64, 2 (2001), 025102."},{"key":"e_1_3_2_1_32_1","volume-title":"Advances in Neural Information Processing Systems","volume":"27","author":"Nickel Maximilian","year":"2014","unstructured":"Maximilian Nickel, Xueyan Jiang, and Volker Tresp. 2014. Reducing the rank in relational factorization models by including observable patterns. Advances in Neural Information Processing Systems, Vol. 27 (2014)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614769"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1214\/11-AOS887"},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Machine Learning. PMLR, 31613-31632","author":"Shirzad Hamed","year":"2023","unstructured":"Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J Sutherland, and Ali Kemal Sinop. 2023. Exphormer: Sparse transformers for graphs. In International Conference on Machine Learning. PMLR, 31613-31632."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672025"},{"key":"e_1_3_2_1_37_1","volume-title":"Harish Kumar Shakya, and Neeraj Kumar","author":"Singh Shashank Sheshar","year":"2024","unstructured":"Shashank Sheshar Singh, Samya Muhuri, Shivansh Mishra, Divya Srivastava, Harish Kumar Shakya, and Neeraj Kumar. 2024. Social network analysis: A survey on process, tools, and application. ACM computing surveys, Vol. 56, 8 (2024), 1-39."},{"key":"e_1_3_2_1_38_1","volume-title":"International Conference on Learning Representations.","author":"Srinivasan Balasubramaniam","year":"2019","unstructured":"Balasubramaniam Srinivasan and Bruno Ribeiro. 2019. On the Equivalence between Positional Node Embeddings and Structural Graph Representations. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2019.2934157"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.3150\/21-BEJ1376"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1214\/17-AOS1623"},{"key":"e_1_3_2_1_42_1","first-page":"556","article-title":"Stick-breaking construction for the Indian buffet process. In Artificial intelligence and statistics","author":"Teh Yee Whye","year":"2007","unstructured":"Yee Whye Teh, Dilan Gr\u00fcr, and Zoubin Ghahramani. 2007. Stick-breaking construction for the Indian buffet process. In Artificial intelligence and statistics. PMLR, 556-563.","journal-title":"PMLR"},{"key":"e_1_3_2_1_43_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_44_1","volume-title":"Neural Common Neighbor with Completion for Link Prediction. In International Conference on Learning Representations.","author":"Wang Xiyuan","year":"2024","unstructured":"Xiyuan Wang, Haotong Yang, and Muhan Zhang. 2024. Neural Common Neighbor with Completion for Link Prediction. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1986"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.3150\/23-BEJ1602"},{"key":"e_1_3_2_1_47_1","volume-title":"A Degree-corrected Stochastic Block Model for Community Discovery in Signed Networks with Heterogeneous Degree Distributions. Pattern Recognition","author":"Yang Zhejian","year":"2025","unstructured":"Zhejian Yang, Yang Li, Bo Yu, Jifeng Hu, and Hechang Chen. 2025a. A Degree-corrected Stochastic Block Model for Community Discovery in Signed Networks with Heterogeneous Degree Distributions. Pattern Recognition (2025), 112285."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.5677772"},{"key":"e_1_3_2_1_49_1","volume-title":"Do transformers really perform badly for graph representation? Advances in neural information processing systems","author":"Ying Chengxuan","year":"2021","unstructured":"Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021. Do transformers really perform badly for graph representation? Advances in neural information processing systems, Vol. 34 (2021), 28877-28888."},{"key":"e_1_3_2_1_50_1","first-page":"13683","article-title":"Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction","volume":"34","author":"Yun Seongjun","year":"2021","unstructured":"Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, and Hyunwoo J Kim. 2021. Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction. Advances in Neural Information Processing Systems, Vol. 34 (2021), 13683-13694.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_51_1","volume-title":"Link prediction based on graph neural networks. Advances in neural information processing systems","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_52_1","first-page":"9061","article-title":"Labeling trick: A theory of using graph neural networks for multi-node representation learning","volume":"34","author":"Zhang Muhan","year":"2021","unstructured":"Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin. 2021. Labeling trick: A theory of using graph neural networks for multi-node representation learning. Advances in Neural Information Processing Systems, Vol. 34 (2021), 9061-9073.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_53_1","volume-title":"International conference on machine learning. PMLR, 4072-4081","author":"Zhao He","year":"2017","unstructured":"He Zhao, Lan Du, and Wray Buntine. 2017. Leveraging node attributes for incomplete relational data. In International conference on machine learning. PMLR, 4072-4081."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33461"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3547817"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3591771"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjb\/e2009-00335-8"},{"key":"e_1_3_2_1_58_1","volume-title":"Max-margin nonparametric latent feature models for link prediction. arXiv preprint arXiv:1602.07428","author":"Zhu Jun","year":"2016","unstructured":"Jun Zhu, Jiaming Song, and Bei Chen. 2016. Max-margin nonparametric latent feature models for link prediction. arXiv preprint arXiv:1602.07428 (2016)."},{"key":"e_1_3_2_1_59_1","volume-title":"Neural bellman-ford networks: A general graph neural network framework for link prediction. Advances in neural information processing systems","author":"Zhu Zhaocheng","year":"2021","unstructured":"Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang. 2021. Neural bellman-ford networks: A general graph neural network framework for link prediction. Advances in neural information processing systems, Vol. 34 (2021), 29476-29490."}],"event":{"name":"WWW '26: The ACM Web Conference 2026","location":"Dubai United Arab Emirates","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2026"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774904.3792497","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T07:54:34Z","timestamp":1783151674000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774904.3792497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":59,"alternative-id":["10.1145\/3774904.3792497","10.1145\/3774904"],"URL":"https:\/\/doi.org\/10.1145\/3774904.3792497","relation":{},"subject":[],"published":{"date-parts":[[2026,4,12]]},"assertion":[{"value":"2026-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}