{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:45:46Z","timestamp":1787017546268,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Ant Group through Ant Research Intern Program"},{"name":"National Key Research and Development Plan of China","award":["2023YFB4502305"],"award-info":[{"award-number":["2023YFB4502305"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,20]]},"DOI":"10.1145\/3690624.3709223","type":"proceedings-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:48:32Z","timestamp":1743792512000},"page":"1527-1538","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph Triple Attention Networks: A Decoupled Perspective"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6804-542X","authenticated-orcid":false,"given":"Xiaotang","family":"Wang","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8950-383X","authenticated-orcid":false,"given":"Yun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8431-3703","authenticated-orcid":false,"given":"Haizhou","family":"Shi","sequence":"additional","affiliation":[{"name":"Rutgers University, New Brunswick, New Jersey, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3440-9675","authenticated-orcid":false,"given":"Yongchao","family":"Liu","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3472-6102","authenticated-orcid":false,"given":"Chuntao","family":"Hong","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Proc. of ICLR.","author":"Alon Uri","year":"2021","unstructured":"Uri Alon and Eran Yahav. 2021. On the Bottleneck of Graph Neural Networks and its Practical Implications. In Proc. of ICLR."},{"key":"e_1_3_2_2_2_1","volume-title":"Bronstein","author":"Bouritsas Giorgos","year":"2023","unstructured":"Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M. Bronstein. 2023. Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. TPAMI (2023), 657--668."},{"key":"e_1_3_2_2_3_1","volume-title":"Proc. of ICLR.","author":"Brody Shaked","year":"2022","unstructured":"Shaked Brody, Uri Alon, and Eran Yahav. 2022. How Attentive are Graph Attention Networks?. In Proc. of ICLR."},{"key":"e_1_3_2_2_4_1","volume-title":"Borgwardt","author":"Chen Dexiong","year":"2022","unstructured":"Dexiong Chen, Leslie O'Bray, and Karsten M. Borgwardt. 2022. Structure-Aware Transformer for Graph Representation Learning. In Proc. of ICML. 3469--3489."},{"key":"e_1_3_2_2_5_1","unstructured":"Jinsong Chen Kaiyuan Gao Gaichao Li and Kun He. 2023. NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs. arXiv:2206.04910 [cs.LG]"},{"key":"e_1_3_2_2_6_1","volume-title":"Proc. of ICML. 1725--1735","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In Proc. of ICML. 1725--1735."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_2_8_1","volume-title":"Proc. of ICLR.","author":"Chien Eli","year":"2021","unstructured":"Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2021. Adaptive Universal Generalized PageRank Graph Neural Network. In Proc. of ICLR."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481916"},{"key":"e_1_3_2_2_10_1","unstructured":"Vijay Prakash Dwivedi and Xavier Bresson. 2020. A Generalization of Transformer Networks to Graphs."},{"key":"e_1_3_2_2_11_1","volume-title":"Thomas Laurent, Yoshua Bengio, and Xavier Bresson.","author":"Dwivedi Vijay Prakash","year":"2020","unstructured":"Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2020. Benchmarking Graph Neural Networks."},{"key":"e_1_3_2_2_12_1","volume-title":"Proc. of ICLR.","author":"Dwivedi Vijay Prakash","year":"2022","unstructured":"Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2022. Graph Neural Networks with Learnable Structural and Positional Representations. In Proc. of ICLR."},{"key":"e_1_3_2_2_13_1","volume-title":"Anh Tuan Luu, and Dominique Beaini","author":"Dwivedi Vijay Prakash","year":"2022","unstructured":"Vijay Prakash Dwivedi, Ladislav Ramp\u00e1\u0161ek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini. 2022. Long Range Graph Benchmark."},{"key":"e_1_3_2_2_14_1","volume-title":"GRAND: Scalable Graph Random Neural Networks. arXiv:2203.06389 [cs.LG]","author":"Feng Wenzheng","year":"2022","unstructured":"Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, and Jie Tang. 2022. GRAND: Scalable Graph Random Neural Networks. arXiv:2203.06389 [cs.LG]"},{"key":"e_1_3_2_2_15_1","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."},{"key":"e_1_3_2_2_16_1","unstructured":"Matthias Fey and Jan Eric Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric."},{"key":"e_1_3_2_2_17_1","volume-title":"Dahl","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 Proc. of ICML. 1263--1272."},{"key":"e_1_3_2_2_18_1","volume-title":"Proc. of NeurIPS. 1024--1034","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Proc. of NeurIPS. 1024--1034."},{"key":"e_1_3_2_2_19_1","volume-title":"Proc. of NeurIPS. 2641--2653","author":"He Tiantian","year":"2021","unstructured":"Tiantian He, Yew Soon Ong, and Lu Bai. 2021. Learning Conjoint Attentions for Graph Neural Nets. In Proc. of NeurIPS. 2641--2653."},{"key":"e_1_3_2_2_20_1","volume-title":"Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning.","author":"He Xiaoxin","year":"2023","unstructured":"Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi. 2023. Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning."},{"key":"e_1_3_2_2_21_1","volume-title":"Proc. of NeurIPS.","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. In Proc. of NeurIPS."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Md Shamim Hussain Mohammed J. Zaki and Dharmashankar Subramanian. 2022. Global Self-Attention as a Replacement for Graph Convolution.","DOI":"10.1145\/3534678.3539296"},{"key":"e_1_3_2_2_23_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 Proc. of ICLR."},{"key":"e_1_3_2_2_24_1","volume-title":"Proc. of NeurIPS. 21618--21629","author":"Kreuzer Devin","year":"2021","unstructured":"Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent L\u00e9tourneau, and Prudencio Tossou. 2021. Rethinking Graph Transformers with Spectral Attention. In Proc. of NeurIPS. 21618--21629."},{"key":"e_1_3_2_2_25_1","unstructured":"Soo Yong Lee Fanchen Bu Jaemin Yoo and Kijung Shin. 2023. Towards Deep Attention in Graph Neural Networks: Problems and Remedies."},{"key":"e_1_3_2_2_26_1","volume-title":"Proc. of NeurIPS.","author":"Li Pan","year":"2020","unstructured":"Pan Li, YanbangWang, HongweiWang, and Jure Leskovec. 2020. Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning. In Proc. of NeurIPS."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645316"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583451"},{"key":"e_1_3_2_2_30_1","unstructured":"Liheng Ma Chen Lin Derek Lim Adriana Romero-Soriano Puneet K. Dokania Mark Coates Philip Torr and Ser-Nam Lim. 2023. Graph Inductive Biases in Transformers without Message Passing. arXiv:2305.17589 [cs.LG]"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"e_1_3_2_2_32_1","volume-title":"Proc. of ICLR.","author":"Oono Kenta","year":"2020","unstructured":"Kenta Oono and Taiji Suzuki. 2020. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In Proc. of ICLR."},{"key":"e_1_3_2_2_33_1","volume-title":"Proc. of ICLR.","author":"Pei Hongbin","year":"2020","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-GCN: Geometric Graph Convolutional Networks. In Proc. of ICLR."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220077"},{"key":"e_1_3_2_2_35_1","volume-title":"Anh Tuan Luu, Guy Wolf, and Dominique Beaini.","author":"Ramp\u00e1\u0161ek Ladislav","year":"2023","unstructured":"Ladislav Ramp\u00e1\u0161ek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini. 2023. Recipe for a General, Powerful, Scalable Graph Transformer. arXiv:2205.12454 [cs.LG]"},{"key":"e_1_3_2_2_36_1","volume-title":"Collins","author":"Stokes Jonathan M.","year":"2020","unstructured":"Jonathan M. Stokes, Kevin Yang, Kyle Swanson,Wengong Jin, and James J. Collins. 2020. A Deep Learning Approach to Antibiotic Discovery. Cell (2020), 475--483."},{"key":"e_1_3_2_2_37_1","volume-title":"Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein.","author":"Topping Jake","year":"2022","unstructured":"Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein. 2022. Understanding over-squashing and bottlenecks on graphs via curvature. In Proc. of ICLR."},{"key":"e_1_3_2_2_38_1","volume-title":"Proc. of ICLR.","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In Proc. of ICLR."},{"key":"e_1_3_2_2_39_1","unstructured":"Xiaotang Wang Yun Zhu Haizhou Shi Yongchao Liu and Chuntao Hong. 2024. UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks. arXiv:2407.19420 [cs.LG]"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591716"},{"key":"e_1_3_2_2_41_1","unstructured":"Qitian Wu Wentao Zhao Zenan Li David Wipf and Junchi Yan. 2023. Node-Former: A Scalable Graph Structure Learning Transformer for Node Classification."},{"key":"e_1_3_2_2_42_1","unstructured":"Qitian Wu Wentao Zhao Chenxiao Yang Hengrui Zhang Fan Nie Haitian Jiang Yatao Bian and Junchi Yan. 2023. SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations."},{"key":"e_1_3_2_2_43_1","unstructured":"Yujie Xing Xiao Wang Yibo Li Hai Huang and Chuan Shi. 2024. Less is More: on the Over-Globalizing Problem in Graph Transformers."},{"key":"e_1_3_2_2_44_1","volume-title":"Proc. of ICLR.","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu,Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In Proc. of ICLR."},{"key":"e_1_3_2_2_45_1","volume-title":"Proc. of ICML. 40--48","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. 2016. Revisiting Semi-Supervised Learning with Graph Embeddings. In Proc. of ICML. 40--48."},{"key":"e_1_3_2_2_46_1","unstructured":"Chengxuan Ying Tianle Cai Shengjie Luo Shuxin Zheng Guolin Ke Di He Yanming Shen and Tie-Yan Liu. 2021. Do Transformers Really Perform Bad for Graph Representation? arXiv:2106.05234 [cs.LG]"},{"key":"e_1_3_2_2_47_1","volume-title":"Prasanna","author":"Zeng Hanqing","year":"2020","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna. 2020. GraphSAINT: Graph Sampling Based Inductive Learning Method. In Proc. of ICLR."},{"key":"e_1_3_2_2_48_1","volume-title":"Proc. of ICLR.","author":"Zhang Kai","year":"2020","unstructured":"Kai Zhang, Yaokang Zhu, Jun Wang, and Jie Zhang. 2020. Adaptive Structural Fingerprints for Graph Attention Networks. In Proc. of ICLR."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-023-00558-4"},{"key":"e_1_3_2_2_50_1","volume-title":"Adaprop: Learning adaptive propagation for graph neural network based knowledge graph reasoning. In SIGKDD. 3446--3457.","author":"Zhang Yongqi","year":"2023","unstructured":"Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, and Bo Han. 2023. Adaprop: Learning adaptive propagation for graph neural network based knowledge graph reasoning. In SIGKDD. 3446--3457."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/527"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"crossref","unstructured":"Yun Zhu Haizhou Shi Xiaotang Wang Yongchao Liu Yaoke Wang Boci Peng Chuntao Hong and Siliang Tang. 2024. GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs.","DOI":"10.1145\/3696410.3714801"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2024\/634"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645439"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709223","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690624.3709223","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T15:46:03Z","timestamp":1755359163000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":54,"alternative-id":["10.1145\/3690624.3709223","10.1145\/3690624"],"URL":"https:\/\/doi.org\/10.1145\/3690624.3709223","relation":{},"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-07-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}