{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:19:54Z","timestamp":1766067594766,"version":"3.41.0"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T00:00:00Z","timestamp":1677024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["91746209, 61772102, and 62176036"],"award-info":[{"award-number":["91746209, 61772102, and 62176036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"crossref","award":["SFI\/12\/RC\/2289_P2"],"award-info":[{"award-number":["SFI\/12\/RC\/2289_P2"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Liaoning Collaborative Fund","award":["2020-HYLH-17"],"award-info":[{"award-number":["2020-HYLH-17"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>Temporal link prediction (TLP) is among the most important graph learning tasks, capable of predicting dynamic, time-varying links within networks. The key problem of TLP is how to explore potential link-evolving tendency from the increasing number of links over time. There exist three major challenges toward solving this problem: temporal nonlinear sparsity, weak serial correlation, and discontinuous structural dynamics. In this article, we propose a novel transfer learning model, called DNformer, to predict temporal link sequence in dynamic networks. The structural dynamic evolution is sequenced into consecutive links one by one over time to inhibit temporal nonlinear sparsity. The self-attention of the model is used to capture the serial correlation between the input and output link sequences. Moreover, our structural encoding is designed to obtain changing structures from the consecutive links and to learn the mapping between link sequences. This structural encoding consists of two parts: the node clustering encoding of each link and the link similarity encoding between links. These encodings enable the model to perceive the importance and correlation of links. Furthermore, we introduce a measurement of structural similarity in the loss function for the structural differences of link sequences. The experimental results demonstrate that our model outperforms other state-of-the-art TLP methods such as Transformer, TGAT, and EvolveGCN. It achieves the three highest AUC and four highest precision scores in five different representative dynamic networks problems.<\/jats:p>","DOI":"10.1145\/3551892","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T11:18:00Z","timestamp":1659439080000},"page":"1-21","update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["DNformer: Temporal Link Prediction with Transfer Learning in Dynamic Networks"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0003-4123-3292","authenticated-orcid":false,"given":"Xin","family":"Jiang","sequence":"first","affiliation":[{"name":"Dalian Maritime University, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0001-8967-5669","authenticated-orcid":false,"given":"Zhengxin","family":"Yu","sequence":"additional","affiliation":[{"name":"Lancaster University, Lancashire, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0001-7965-1831","authenticated-orcid":false,"given":"Chao","family":"Hai","sequence":"additional","affiliation":[{"name":"Dalian Maritime University, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0001-9296-9975","authenticated-orcid":false,"given":"Hongbo","family":"Liu","sequence":"additional","affiliation":[{"name":"Dalian Maritime University, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0003-2396-1704","authenticated-orcid":false,"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0002-6173-6607","authenticated-orcid":false,"given":"Tomas","family":"Ward","sequence":"additional","affiliation":[{"name":"Dublin City University, Leinster, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,22]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Jimmy Lei Ba Jamie Ryan Kiros and Geoffrey E. Hinton. 2016. Layer normalization. arXiv e-prints arXiv: stat.ML\/1607.06450."},{"key":"e_1_3_2_3_2","first-page":"7464","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","author":"Cai Deng","year":"2020","unstructured":"Deng Cai and Wai Lam. 2020. Graph transformer for graph-to-sequence learning. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI Press, Palo Alto, CA, 7464\u20137471."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2019.2932913"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3064092"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_7_2","first-page":"2086","volume-title":"Proceedings of the 27th AAAI Conference on Artificial Intelligence","author":"Du Lun","year":"2018","unstructured":"Lun Du, Yun Wang, Guojie Song, Zhicong Lu, and Junshan Wang. 2018. Dynamic network embedding: An extended approach for skip-gram based network embedding. In Proceedings of the 27th AAAI Conference on Artificial Intelligence. AAAI Press, Palo Alto, CA, 2086\u20132092."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/1921632.1921636"},{"key":"e_1_3_2_9_2","unstructured":"Vijay Prakash Dwivedi and Xavier Bresson. 2020. A generalization of transformer networks to graphs. arXiv e-prints arXiv:2012.09699."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3301303"},{"key":"e_1_3_2_11_2","first-page":"5036","volume-title":"Proceedings of INTERSPEECH","author":"Gulati Anmol","year":"2020","unstructured":"Anmol Gulati, James Qin, ChungCheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, and Yonghui Wu. 2020. Conformer: Convolution-augmented transformer for speech recognition. In Proceedings of INTERSPEECH. International Speech Communication Association, Shanghai, China, 5036\u20135040."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3046511"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/2541268.2541270"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/BDCloud-SocialCom-SustainCom.2016.63"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1238"},{"key":"e_1_3_2_18_2","first-page":"289","volume-title":"Proceedings of the 2014 SIAM International Conference on Data Mining","volume":"1","author":"Li Xiaoyi","year":"2014","unstructured":"Xiaoyi Li, Nan Du, Hui Li, Kang Li, Jing Gao, and Aidong Zhang. 2014. A deep learning approach to link prediction in dynamic networks. In Proceedings of the 2014 SIAM International Conference on Data Mining, Vol. 1. Society for Industrisl and Applied Mathematics, Philadelphia, Pennsylvania, 289\u2013297."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1002\/asi.20591"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3022669"},{"key":"e_1_3_2_21_2","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Liu Xuanqing","year":"2020","unstructured":"Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, and Cho-Jui Hsieh. 2020. Learning to encode position for transformer with continuous dynamical model. In Proceedings of the 37th International Conference on Machine Learning. JMLR.org, Vienna, AUSTRIA, Article 587, 9 pages."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_2_23_2","first-page":"5679","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","author":"Pareja Aldo","year":"2020","unstructured":"Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, and Charles E. Leiserson. 2020. EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI Press, Palo Alto, CA, 5679\u20135681."},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/2723694"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-018-9943-9"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3057082"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3026311"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3108513"},{"key":"e_1_3_2_30_2","first-page":"12559","volume-title":"Proceedings of Neural Information Processing Systems","volume":"33","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020. Self-supervised graph transformer on large-scale molecular data. In Proceedings of Neural Information Processing Systems, Vol. 33. Curran Associates, Inc., Vancouver, Canada, 12559\u201312571."},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3424672"},{"key":"e_1_3_2_32_2","first-page":"4292","volume-title":"Proceedings of AAAI Conference on Artificial Intelligence.","author":"Rossi Ryan A.","year":"2015","unstructured":"Ryan A. Rossi and Nesreen K. Ahmed. 2015. The network data repository with interactive graph analytics and visualization. In Proceedings of AAAI Conference on Artificial Intelligence.AAAI Press, Palo Alto, CA, 4292\u20134293. Retrieved from https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/http\/networkrepository.com."},{"key":"e_1_3_2_33_2","first-page":"540","volume-title":"Proceedings of 8th IEEE international conference on data mining","author":"Sharan Umang","year":"2008","unstructured":"Umang Sharan and Jennifer Neville. 2008. Temporal-relational classifiers for prediction in evolving domains. In Proceedings of 8th IEEE international conference on data mining. IEEE Computer Society, Los Alamitos, CA, 540\u2013549."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/2968451"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2016.2638321"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.103"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295349"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2728527"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511815478"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_41_2","first-page":"1","volume-title":"Proceedings of International Conference on Learning Representations","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. In Proceedings of International Conference on Learning Representations. OpenReview.net, Ithaca, NY, 1."},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2887230"},{"key":"e_1_3_2_43_2","first-page":"1","volume-title":"Proceedings of International Conference on Learning Representations","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In Proceedings of International Conference on Learning Representations. OpenReview.net, Ithaca, NY, 1."},{"key":"e_1_3_2_44_2","first-page":"7444","volume-title":"Proceedings of the 32th AAAI Conference on Artificial Intelligence","author":"Yan Sijie","year":"2018","unstructured":"Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the 32th AAAI Conference on Artificial Intelligence. AAAI Press, Palo Alto, CA, 7444\u20137452."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2920268"},{"key":"e_1_3_2_46_2","first-page":"28877","volume-title":"Proceedings of Neural Information Processing Systems","volume":"34","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? In Proceedings of Neural Information Processing Systems, Vol. 34. Curran Associates, Inc., Vancouver, Canada, 28877\u201328888."},{"key":"e_1_3_2_47_2","first-page":"455","volume-title":"Proceedings of the 10th ACM International Conference on Web Search and Data Mining","author":"Yu Wenchao","year":"2017","unstructured":"Wenchao Yu, Charu C. Aggarwal, and Wei Wang. 2017. Temporally factorized network modeling for evolutionary network analysis. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, New York, NY, 455\u2013464."},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.5555\/3327345.3327423"},{"key":"e_1_3_2_49_2","first-page":"571","volume-title":"Proceedings of the 32th AAAI Conference on Artificial Intelligence","author":"Zhou Lekui","year":"2018","unstructured":"Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. 2018. Dynamic network embedding by modeling triadic closure process. In Proceedings of the 32th AAAI Conference on Artificial Intelligence. AAAI Press, Palo Alto, CA, 571\u2013578."},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2591009"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3551892","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/3551892","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:17Z","timestamp":1750186817000},"score":1,"resource":{"primary":{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3551892"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,22]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6,30]]}},"alternative-id":["10.1145\/3551892"],"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1145\/3551892","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2023,2,22]]},"assertion":[{"value":"2021-11-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-20","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}