{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:38Z","timestamp":1750309538817,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302511, 62372458"],"award-info":[{"award-number":["62302511, 62372458"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Strengthening Program (Young Elite Scientists Sponsorship Program)","award":["2023-JCJQ-QT-048"],"award-info":[{"award-number":["2023-JCJQ-QT-048"]}]},{"name":"Independent Innovation Science foundation project of National University of Defense Technology","award":["23-ZZCX-JDZ-43"],"award-info":[{"award-number":["23-ZZCX-JDZ-43"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,28]]},"DOI":"10.1145\/3696410.3714564","type":"proceedings-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T23:08:29Z","timestamp":1745363309000},"page":"3607-3617","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Triangle Matters! TopDyG: Topology-aware Transformer for Link Prediction on Dynamic Graphs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6070-1592","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9221-5731","authenticated-orcid":false,"given":"Fei","family":"Cai","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8486-5631","authenticated-orcid":false,"given":"Jianming","family":"Zheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Wuxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5484-1743","authenticated-orcid":false,"given":"Zhiqiang","family":"Pan","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4470-9655","authenticated-orcid":false,"given":"Wanyu","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic Countermeasures, National University of Defense Technology, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5234-0592","authenticated-orcid":false,"given":"Honghui","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3919-8598","authenticated-orcid":false,"given":"Chonghao","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Science","volume":"286","author":"Barab\u00e1si Albert-L\u00e1szl\u00f3","year":"1999","unstructured":"Albert-L\u00e1szl\u00f3 Barab\u00e1si and R\u00e9ka Albert. 1999. Emergence of Scaling in Random Networks. Science, Vol. 286, 5439 (1999), 509--512."},{"key":"e_1_3_2_1_2_1","unstructured":"Tom B. Brown Benjamin Mann Nick Ryder and et al. 2020. Language Models are Few-Shot Learners. In NeurIPS. 1877--1901."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Chong Chen Weizhi Ma Min Zhang and et al. 2021. Graph Heterogeneous Multi-Relational Recommendation. In AAAI. AAAI Press 3958--3966.","DOI":"10.1609\/aaai.v35i5.16515"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Weilin Cong Yanhong Wu Yuandong Tian and et al. 2023a. DyFormer : A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability. In SDM. SIAM 442--450.","DOI":"10.1137\/1.9781611977653.ch50"},{"key":"e_1_3_2_1_5_1","unstructured":"Weilin Cong Si Zhang Jian Kang and et al. 2023b. Do We Really Need Complicated Model Architectures For Temporal Networks?. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Yin Cui Menglin Jia Tsung-Yi Lin and et al. 2019. Class-Balanced Loss Based on Effective Number of Samples. In CVPR. Computer Vision Foundation \/ IEEE 9268--9277.","DOI":"10.1109\/CVPR.2019.00949"},{"key":"e_1_3_2_1_7_1","volume-title":"Alex Borges Vieira, and Artur Ziviani","author":"Tenorio de Barros Claudio Daniel","year":"2023","unstructured":"Claudio Daniel Tenorio de Barros, Matheus R. F. Mendon\u00e7a, Alex Borges Vieira, and Artur Ziviani. 2023. A Survey on Embedding Dynamic Graphs. ACM Comput. Surv., Vol. 55, 2 (2023), 10:1--10:37."},{"key":"e_1_3_2_1_8_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1)","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1). Association for Computational Linguistics, 4171--4186."},{"key":"e_1_3_2_1_9_1","volume-title":"Words: Transformers for Image Recognition at Scale. In ICLR. OpenReview.net.","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, and et al. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_10_1","first-page":"17","article-title":"On the evolution of random graphs","volume":"5","author":"Erd\u0151s Paul L.","year":"1960","unstructured":"Paul L. Erd\u0151s and Alfr\u00e9d R\u00e9nyi. 1960. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci, Vol. 5, 1 (1960), 17--60.","journal-title":"Publ. Math. Inst. Hung. Acad. Sci"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li and et al. 2019. Graph Neural Networks for Social Recommendation. In WWW. ACM 417--426.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_12_1","unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS. 1024--1034."},{"key":"e_1_3_2_1_13_1","article-title":"The MovieLens Datasets","volume":"5","author":"Maxwell Harper F.","year":"2016","unstructured":"F. Maxwell Harper and Joseph A. Konstan. 2016. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst., Vol. 5, 4 (2016), 19:1--19:19.","journal-title":"History and Context. ACM Trans. Interact. Intell. Syst."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Anshul Kanakia Zhihong Shen Darrin Eide and et al. 2019. A Scalable Hybrid Research Paper Recommender System for Microsoft Academic. In WWW. ACM 2893--2899.","DOI":"10.1145\/3308558.3313700"},{"key":"e_1_3_2_1_15_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR (Poster). OpenReview.net."},{"key":"e_1_3_2_1_16_1","unstructured":"Devin Kreuzer Dominique Beaini William L. Hamilton and et al. 2021. Rethinking Graph Transformers with Spectral Attention. In NeurIPS. 21618--21629."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Srijan Kumar Xikun Zhang and Jure Leskovec. 2019. Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks. In KDD. ACM 1269--1278.","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Jure Leskovec Jon M. Kleinberg and Christos Faloutsos. 2005. Graphs over time: densification laws shrinking diameters and possible explanations. In KDD. ACM 177--187.","DOI":"10.1145\/1081870.1081893"},{"key":"e_1_3_2_1_19_1","volume-title":"Zheng Zhang, Minlie Huang, and Tat-Seng Chua.","author":"Liao Lizi","year":"2021","unstructured":"Lizi Liao, Le Hong Long, Zheng Zhang, Minlie Huang, and Tat-Seng Chua. 2021. MMConv: An Environment for Multimodal Conversational Search across Multiple Domains. In SIGIR. ACM, 675--684."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"e_1_3_2_1_21_1","unstructured":"Zhining Liu Ruizhong Qiu Zhichen Zeng and et al. 2024. Class-Imbalanced Graph Learning without Class Rebalancing. In ICML. 31747--31772."},{"key":"e_1_3_2_1_22_1","volume-title":"Ankit Singh Rawat, and et al","author":"Menon Aditya Krishna","year":"2021","unstructured":"Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, and et al. 2021. Long-tail learning via logit adjustment. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_23_1","unstructured":"Tom\u00e1s Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In ICLR (Workshop Poster)."},{"key":"e_1_3_2_1_24_1","volume-title":"Network Motifs: Simple Building Blocks of Complex Networks. Science","volume":"298","author":"Milo R.","year":"2002","unstructured":"R. Milo, S. Shen-Orr, S. Itzkovitz, and et al. 2002. Network Motifs: Simple Building Blocks of Complex Networks. Science, Vol. 298, 5594 (2002), 824--827."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Erxue Min Yu Rong Tingyang Xu and et al. 2022. Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer. In SIGIR. ACM 353--362.","DOI":"10.1145\/3477495.3532031"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Mehrnoosh Mirtaheri Ryan A. Rossi Sungchul Kim and et al. 2024. Tackling Long-Tail Entities for Temporal Knowledge Graph Completion. In WWW (Companion Volume). ACM 497--500.","DOI":"10.1145\/3589335.3651565"},{"key":"e_1_3_2_1_27_1","article-title":"Collaborative Graph Learning for Session-based Recommendation","volume":"40","author":"Pan Zhiqiang","year":"2022","unstructured":"Zhiqiang Pan, Fei Cai, Wanyu Chen, and et al. 2022. Collaborative Graph Learning for Session-based Recommendation. ACM Trans. Inf. Syst., Vol. 40, 4 (2022), 72:1--72:26.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/1543767.1543769"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Aldo Pareja Giacomo Domeniconi Jie Chen Tengfei Ma and et al. 2020. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. In AAAI. AAAI Press 5363--5370.","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"e_1_3_2_1_30_1","unstructured":"Alec Radford Jeff Wu Rewon Child and et al. 2019. Language Models are Unsupervised Multitask Learners. (2019)."},{"key":"e_1_3_2_1_31_1","volume-title":"Vijay Prakash Dwivedi, and et al","author":"Ramp\u00e1sek Ladislav","year":"2022","unstructured":"Ladislav Ramp\u00e1sek, Michael Galkin, Vijay Prakash Dwivedi, and et al. 2022. Recipe for a General, Powerful, Scalable Graph Transformer. In NeurIPS. 14501--14515."},{"key":"e_1_3_2_1_32_1","unstructured":"Emanuele Rossi Ben Chamberlain Fabrizio Frasca and et al. 2020. Temporal Graph Networks for Deep Learning on Dynamic Graphs. In ICML (Workshop)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Aravind Sankar Yanhong Wu Liang Gou and et al. 2020. DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks. In WSDM. ACM 519--527.","DOI":"10.1145\/3336191.3371845"},{"key":"e_1_3_2_1_34_1","volume-title":"Graph Transformers: A Survey. CoRR","author":"Shehzad Ahsan","year":"2024","unstructured":"Ahsan Shehzad, Feng Xia, Shagufta Abid, and et al. 2024. Graph Transformers: A Survey. CoRR, Vol. abs\/2407.09777 (2024)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Xiran Song Jianxun Lian Hong Huang and et al. 2023. xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction. In WWW. ACM 349--359.","DOI":"10.1145\/3543507.3583340"},{"key":"e_1_3_2_1_36_1","unstructured":"Rakshit Trivedi Mehrdad Farajtabar Prasenjeet Biswal and Hongyuan Zha. 2019. DyRep: Learning Representations over Dynamic Graphs. In ICLR (Poster). OpenReview.net."},{"key":"e_1_3_2_1_37_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova and et al. 2018. Graph Attention Networks. In ICLR (Poster). OpenReview.net."},{"key":"e_1_3_2_1_38_1","volume-title":"BEVT: BERT Pretraining of Video Transformers","author":"Wang Rui","year":"2022","unstructured":"Rui Wang, Dongdong Chen, Zuxuan Wu, and et al. 2022. BEVT: BERT Pretraining of Video Transformers. In CVPR. IEEE, 14713--14723."},{"key":"e_1_3_2_1_39_1","volume-title":"TREND: TempoRal Event and Node Dynamics for Graph Representation Learning. In WWW. ACM, 1159--1169.","author":"Wen Zhihao","year":"2022","unstructured":"Zhihao Wen and Yuan Fang. 2022. TREND: TempoRal Event and Node Dynamics for Graph Representation Learning. In WWW. ACM, 1159--1169."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Yuxia Wu Yuan Fang and Lizi Liao. 2024. On the Feasibility of Simple Transformer for Dynamic Graph Modeling. In WWW. ACM 870--880.","DOI":"10.1145\/3589334.3645622"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_42_1","unstructured":"Da Xu Chuanwei Ruan Evren K\u00f6rpeoglu and et al. 2020. Inductive representation learning on temporal graphs. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_43_1","unstructured":"Chengxuan Ying Tianle Cai Shengjie Luo and et al. 2021. Do Transformers Really Perform Badly for Graph Representation?. In NeurIPS. 28877--28888."},{"key":"e_1_3_2_1_44_1","unstructured":"Le Yu Leilei Sun Bowen Du and Weifeng Lv. 2023. Towards Better Dynamic Graph Learning: New Architecture and Unified Library. In NeurIPS. 67686--67700."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Xingtong Yu Chang Zhou Yuan Fang and et al. 2024. MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs. In WWW. ACM 515--526.","DOI":"10.1145\/3589334.3645423"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3545798"},{"key":"e_1_3_2_1_47_1","volume-title":"A survey of dynamic graph neural networks. CoRR","author":"Zheng Yanping","year":"1821","unstructured":"Yanping Zheng, Lu Yi, and Zhewei Wei. 2024. A survey of dynamic graph neural networks. CoRR, Vol. abs\/2404.18211 (2024)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Yifan Zhu Fangpeng Cong Dan Zhang and et al. 2023. WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window. In KDD. ACM 3650--3662.","DOI":"10.1145\/3580305.3599551"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty294"}],"event":{"name":"WWW '25: The ACM Web Conference 2025","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Sydney NSW Australia","acronym":"WWW '25"},"container-title":["Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714564","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696410.3714564","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:33Z","timestamp":1750295913000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714564"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":49,"alternative-id":["10.1145\/3696410.3714564","10.1145\/3696410"],"URL":"https:\/\/doi.org\/10.1145\/3696410.3714564","relation":{},"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"2025-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}