{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:08:24Z","timestamp":1755220104956,"version":"3.43.0"},"reference-count":68,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:p>Weakly Supervised Entity Alignment (EA) is the task of identifying equivalent entities across diverse knowledge graphs (KGs) using only a limited number of seed alignments. Despite substantial advances in aggregation-based weakly supervised EA, the underlying mechanisms in this setting remain unexplored. In this article, we present a propagation perspective to analyze weakly supervised EA and explain the existing aggregation-based EA models. Our theoretical analysis reveals that these models essentially seek propagation operators for pairwise entity similarities. We further prove that, despite the structural heterogeneity across different KGs, the potentially aligned entities within aggregation-based EA models exhibit isomorphic subgraphs, a fundamental yet underexplored premise of EA. Leveraging this insight, we introduce a potential isomorphism propagation operator to enhance the propagation of neighborhood information across KGs. We develop a general EA framework, PipEA, incorporating this operator to improve the accuracy of every type of aggregation-based model without altering the learning process. Extensive experiments substantiate our theoretical findings and demonstrate PipEA\u2019s significant performance gains over state-of-the-art weakly supervised EA methods. Our work advances the field and enhances our comprehension of aggregation-based weakly supervised EA.<\/jats:p>","DOI":"10.1145\/3742436","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T11:48:54Z","timestamp":1748864934000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3072-7422","authenticated-orcid":false,"given":"Haifeng","family":"Sun","sequence":"first","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8909-5774","authenticated-orcid":false,"given":"Yuanyi","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8514-8810","authenticated-orcid":false,"given":"Han","family":"Li","sequence":"additional","affiliation":[{"name":"China Unicom, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9250-4163","authenticated-orcid":false,"given":"Wei","family":"Tang","sequence":"additional","affiliation":[{"name":"Huawei Translation Services Center, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3345-1732","authenticated-orcid":false,"given":"Zirui","family":"Zhuang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0829-4624","authenticated-orcid":false,"given":"Qi","family":"Qi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2182-2228","authenticated-orcid":false,"given":"Jingyu","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-72113-8_4"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532679"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304326"},{"key":"e_1_3_1_5_2","article-title":"Sinkhorn distances: Lightspeed computation of optimal transport","volume":"26","author":"Cuturi Marco","year":"2013","unstructured":"Marco Cuturi. 2013. Sinkhorn distances: Lightspeed computation of optimal transport. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 26.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539331"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.14778\/3489496.3489504"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976700.20"},{"key":"e_1_3_1_9_2","article-title":"Learning-based low-rank approximations","volume":"32","author":"Indyk Piotr","year":"2019","unstructured":"Piotr Indyk, Ali Vakilian, and Yang Yuan. 2019. Learning-based low-rank approximations. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 32.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645720"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOS1414"},{"key":"e_1_3_1_12_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.14778\/3529337.3529355"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1274"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511926"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583328"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106479"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Bing Liu Harrisen Scells Guido Zuccon Wen Hua and Genghong Zhao. 2021. ActiveEA: Active learning for neural entity alignment. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing 3364\u20133374.","DOI":"10.18653\/v1\/2021.emnlp-main.270"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16550"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539289"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511945"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583381"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.515"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.405"},{"key":"e_1_3_1_26_2","first-page":"1263","volume-title":"Proceedings of the 30th ACM International Conference on Information and Knowledge Management","author":"Mao Xin","year":"2021","unstructured":"Xin Mao, Wenting Wang, Yuanbin Wu, and Man Lan. 2021. Are negative samples necessary in entity alignment? An approach with high performance, scalability and robustness. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management, 1263\u20131273."},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449897"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.52"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371804"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412001"},{"key":"e_1_3_1_31_2","first-page":"13022","article-title":"A fractional graph Laplacian approach to oversmoothing","volume":"36","author":"Maskey Sohir","year":"2023","unstructured":"Sohir Maskey, Raffaele Paolino, Aras Bacho, and Gitta Kutyniok. 2023. A fractional graph Laplacian approach to oversmoothing. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 36, 13022\u201313063.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_32_2","unstructured":"Lawrence Page Sergey Brin Rajeev Motwani and Terry Winograd. 1998. The PageRank Citation Ranking: Bring Order to the Web. Technical Report. Stanford University."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313646"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_1_35_2","unstructured":"Meng Qu Jian Tang and Yoshua Bengio. 2019. Weakly-supervised knowledge graph alignment with adversarial learning. arXiv:1907.03179. Retrieved from https:\/\/arxiv.org\/abs\/1907.03179"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970739"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68288-4_37"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/611"},{"key":"e_1_3_1_39_2","unstructured":"Zequn Sun Jiacheng Huang Xiaozhou Xu Qijin Chen Weijun Ren and Wei Hu. 2023. What makes entities similar? A similarity flooding perspective for multi-sourced knowledge graph embeddings. arXiv:2306.02622. Retrieved from https:\/\/arxiv.org\/abs\/2306.02622"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407828"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3487553.3524720"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570394"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301297"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186120"},{"key":"e_1_3_1_45_2","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2017. Graph attention networks. arXiv:1710.10903. Retrieved from https:\/\/arxiv.org\/abs\/1710.10903"},{"key":"e_1_3_1_46_2","doi-asserted-by":"crossref","unstructured":"Sheng Wan Shirui Pan Jian Yang and Chen Gong. 2021. Contrastive and generative graph convolutional networks for graph-based semi-supervised learning. In Proceedings of the AAAI Conference on Artificial Intelligence 10049\u201310057.","DOI":"10.1609\/aaai.v35i11.17206"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591730"},{"key":"e_1_3_1_48_2","unstructured":"Yuanyi Wang Haifeng Sun Jingyu Wang Qi Qi Shaoling Sun and Jianxin Liao. 2024. Gradient flow of energy: A general and efficient approach for entity alignment decoding. arXiv:2401.12798. Retrieved from https:\/\/arxiv.org\/abs\/2401.12798"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00274"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1032"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591816"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/733"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557374"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.709"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5696"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.14778\/3377369.3377376"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599400"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330860"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3592052"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482472"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401161"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3638778"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219969"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-4250-3_7"},{"issue":"6","key":"e_1_3_1_66_2","first-page":"2610","article-title":"An experimental study of state-of-the-art entity alignment approaches","volume":"34","author":"Zhao Xiang","year":"2020","unstructured":"Xiang Zhao, Weixin Zeng, Jiuyang Tang, Wei Wang, and Fabian M. Suchanek. 2020. An experimental study of state-of-the-art entity alignment approaches. IEEE Transactions on Knowledge and Data Engineering 34, 6 (2020), 2610\u20132625.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103951"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16606"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3742436","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T21:45:44Z","timestamp":1754603144000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3742436"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"references-count":68,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,8,31]]}},"alternative-id":["10.1145\/3742436"],"URL":"https:\/\/doi.org\/10.1145\/3742436","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2025,8,7]]},"assertion":[{"value":"2024-10-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}