{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T11:18:12Z","timestamp":1782904692337,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","funder":[{"name":"NSF","award":["TIP-2333703"],"award-info":[{"award-number":["TIP-2333703"]}]},{"DOI":"10.13039\/501100006374","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-21-1-2530"],"award-info":[{"award-number":["N00014-21-1-2530"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,8]]},"DOI":"10.1145\/3701716.3715549","type":"proceedings-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T14:10:32Z","timestamp":1750687832000},"page":"976-980","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Few-shot Learning over Graphs Using Topological Prompts"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5403-177X","authenticated-orcid":false,"given":"Jaidev","family":"Goel","sequence":"first","affiliation":[{"name":"Virginia Tech, Blacksburg, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7945-7940","authenticated-orcid":false,"given":"Yuzhou","family":"Chen","sequence":"additional","affiliation":[{"name":"University of California, Riverside, Riverside, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4500-6495","authenticated-orcid":false,"given":"Yulia","family":"Gel","sequence":"additional","affiliation":[{"name":"Virginia Tech, Blacksburg, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Topological Data Analysis","author":"Carlsson Gunnar","unstructured":"Gunnar Carlsson and Rickard Br\u00fcel Gabrielsson. 2020. Topological approaches to deep learning. In Topological Data Analysis. Springer, 119--146."},{"key":"e_1_3_2_1_2_1","volume-title":"Perslay: A neural network layer for persistence diagrams and new graph topological signatures. In AISTATS.","author":"Carri\u00e8re Mathieu","year":"2020","unstructured":"Mathieu Carri\u00e8re, Fr\u00e9d\u00e9ric Chazal, Yuichi Ike, Th\u00e9o Lacombe, Martin Royer, and Yuhei Umeda. 2020. Perslay: A neural network layer for persistence diagrams and new graph topological signatures. In AISTATS."},{"key":"e_1_3_2_1_3_1","volume-title":"An introduction to topological data analysis: fundamental and practical aspects for data scientists. Frontiers in artificial intelligence 4","author":"Chazal Fr\u00e9d\u00e9ric","year":"2021","unstructured":"Fr\u00e9d\u00e9ric Chazal and Bertrand Michel. 2021. An introduction to topological data analysis: fundamental and practical aspects for data scientists. Frontiers in artificial intelligence 4 (2021), 667963."},{"key":"e_1_3_2_1_4_1","unstructured":"Yuzhou Chen Baris Coskunuzer and Yulia Gel. 2021. Topological relational learning on graphs. In NeurIPS."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Yuzhou Chen Miguel Heleno Alexandre Moreira and Yulia R Gel. 2023. Topolog- ical graph convolutional networks solutions for power distribution grid planning. In PAKDD.","DOI":"10.1007\/978-3-031-33374-3_10"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Yuzhou Chen Elena Sizikova and Yulia R Gel. 2022. TopoAttn-Nets: Topological Attention in Graph Representation Learning. In ECML-PKDD. 309--325.","DOI":"10.1007\/978-3-031-26390-3_19"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25870"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Herbert Edelsbrunner John Harer et al. 2008. Persistent homology-a survey. Contemporary mathematics 453 26 (2008) 257--282.","DOI":"10.1090\/conm\/453\/08802"},{"key":"e_1_3_2_1_9_1","unstructured":"Ekin Ergen and Moritz Grillo. 2024. Topological Expressivity of ReLU Neural Networks. In COLT. 1599--1642."},{"key":"e_1_3_2_1_10_1","unstructured":"Taoran Fang Yunchao Zhang Yang Yang Chunping Wang and Lei Chen. 2024. Universal prompt tuning for graph neural networks. In NeurIPS."},{"key":"e_1_3_2_1_11_1","volume-title":"Yang Yang, Chunping Wang, and Lei CHEN.","author":"Fang Taoran","year":"2023","unstructured":"Taoran Fang, Yunchao Mercer Zhang, Yang Yang, Chunping Wang, and Lei CHEN. 2023. Universal Prompt Tuning for Graph Neural Networks. In NeurIPS."},{"key":"e_1_3_2_1_12_1","unstructured":"William L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1025--1035."},{"key":"e_1_3_2_1_13_1","unstructured":"Weihua Hu Bowen Liu Joseph Gomes Marinka Zitnik Percy Liang Vijay Pande and Jure Leskovec. 2020. Strategies for Pre-training Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_1_14_1","unstructured":"Ziniu Hu Yuxiao Dong Kuansan Wang Kai-Wei Chang and Yizhou Sun. 2020. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In SIGKDD."},{"key":"e_1_3_2_1_15_1","unstructured":"Dongkwan Kim and Alice Oh. 2021. How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision. In ICLR."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Th\u00e9o Lacombe Yuichi Ike Mathieu Carriere Fr\u00e9d\u00e9ric Chazal Marc Glisse and Yuhei Umeda. 2021. Topological uncertainty: Monitoring trained neural networks through persistence of activation graphs. In IJCAI.","DOI":"10.24963\/ijcai.2021\/367"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Jintang Li Ruofan Wu Wangbin Sun Liang Chen Sheng Tian Liang Zhu Changhua Meng Zibin Zheng and Weiqiang Wang. 2023. What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders. In SIGKDD. 1268--1279.","DOI":"10.1145\/3580305.3599546"},{"key":"e_1_3_2_1_18_1","volume-title":"GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks. In Web Conf. 417--428","author":"Liu Zemin","year":"2023","unstructured":"Zemin Liu, Xingtong Yu, Yuan Fang, and Xinming Zhang. 2023. GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks. In Web Conf. 417--428."},{"key":"e_1_3_2_1_19_1","volume-title":"Jinmiao Chen, Jiawei Luo, and Xiaoli Li.","author":"Long Yahui","year":"2022","unstructured":"Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Keong Kwoh, Jinmiao Chen, Jiawei Luo, and Xiaoli Li. 2022. Pre-training graph neural networks for link prediction in biomedical networks. Bioinformatics 38, 8 (02 2022), 2254--2262."},{"key":"e_1_3_2_1_20_1","first-page":"1","article-title":"Topology of deep neural networks","volume":"21","author":"Naitzat Gregory","year":"2020","unstructured":"Gregory Naitzat, Andrey Zhitnikov, and Lek-Heng Lim. 2020. Topology of deep neural networks. Journal of Machine Learning Research 21, 184 (2020), 1--40.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Dorcas Ofori-Boateng Jaidev Goel Ivor Cribben and Yulia R Gel. 2024. Graphical Model-Based Lasso for Weakly Dependent Time Series of Tensors. In ECML- PKDD.","DOI":"10.1007\/978-3-031-70362-1_15"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i18.17986"},{"key":"e_1_3_2_1_24_1","unstructured":"Fan-Yun Sun Jordan Hoffman Vikas Verma and Jian Tang. 2019. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In ICLR."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Xiangguo Sun Hong Cheng Jia Li Bo Liu and Jihong Guan. 2023. All in One: Multi-Task Prompting for Graph Neural Networks. In SIGKDD. 2120--2131.","DOI":"10.1145\/3580305.3599256"},{"key":"e_1_3_2_1_26_1","unstructured":"Yu Wang Byoungwook Jang and Alfred Hero. 2020. The sylvester graphical lasso (syglasso). In AISTATS."},{"key":"e_1_3_2_1_27_1","unstructured":"Yaochen Xie Zhao Xu and Shuiwang Ji. 2022. Self-Supervised Representation Learning via Latent Graph Prediction. In ICML."},{"key":"e_1_3_2_1_28_1","unstructured":"Zuoyu Yan Tengfei Ma Liangcai Gao Zhi Tang and Chao Chen. 2021. Link prediction with persistent homology: An interactive view. In ICML."},{"key":"e_1_3_2_1_29_1","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. In NeurIPS, Vol. 33. 5812--5823.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_30_1","volume-title":"Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs","author":"Yu Xingtong","year":"2024","unstructured":"Xingtong Yu, Zhenghao Liu, Yuan Fang, Zemin Liu, Sihong Chen, and Xinming Zhang. 2024. Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs. IEEE Trans. Knowl. Data Eng. (2024)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Haihong Zhao Aochuan Chen Xiangguo Sun Hong Cheng and Jia Li. 2024. All in one and one for all: A simple yet effective method towards cross-domain graph pretraining. In SIGKDD. 4443--4454.","DOI":"10.1145\/3637528.3671913"},{"key":"e_1_3_2_1_32_1","volume-title":"Graph Contrastive Learning with Adaptive Augmentation. In Web Conf. 2069--2080","author":"Zhu Yanqiao","year":"2021","unstructured":"Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021. Graph Contrastive Learning with Adaptive Augmentation. In Web Conf. 2069--2080."},{"key":"e_1_3_2_1_33_1","volume-title":"ProG: A Graph Prompt Learning Benchmark. arXiv:2406.05346","author":"Zi Chenyi","year":"2024","unstructured":"Chenyi Zi, Haihong Zhao, Xiangguo Sun, Yiqing Lin, Hong Cheng, and Jia Li. 2024. ProG: A Graph Prompt Learning Benchmark. arXiv:2406.05346 (2024)."}],"event":{"name":"WWW '25: The ACM Web Conference 2025","location":"Sydney NSW Australia","acronym":"WWW '25","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Companion Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3701716.3715549","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T18:26:26Z","timestamp":1759861586000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715549"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":33,"alternative-id":["10.1145\/3701716.3715549","10.1145\/3701716"],"URL":"https:\/\/doi.org\/10.1145\/3701716.3715549","relation":{},"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"2025-05-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}