{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T12:03:21Z","timestamp":1769774601191,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":48,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819557189","type":"print"},{"value":"9789819557196","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-5719-6_33","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:34:16Z","timestamp":1769718856000},"page":"516-532","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fetan: Enhancing Few-Shot Classification on\u00a0Text-Attributed Graphs with\u00a0In-Context Learning of\u00a0LLMs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0008-3817","authenticated-orcid":false,"given":"Yun","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0287-8867","authenticated-orcid":false,"given":"Minghe","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6346-6719","authenticated-orcid":false,"given":"Jintong","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6902-9299","authenticated-orcid":false,"given":"Tiancheng","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3171-8889","authenticated-orcid":false,"given":"Ge","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"33_CR1","unstructured":"AI@Meta: Llama 3 model card (2024). https:\/\/github.com\/meta-llama\/llama3\/blob\/main\/MODEL_CARD.md"},{"key":"33_CR2","doi-asserted-by":"publisher","unstructured":"Benson, A.R., Gleich, D.F., Leskovec, J.: Higher-order organization of complex networks. Science 353(6295), 163\u2013166 (2016). https:\/\/doi.org\/10.1126\/science.aad9029. https:\/\/www.science.org\/doi\/abs\/10.1126\/science.aad9029","DOI":"10.1126\/science.aad9029"},{"issue":"6","key":"33_CR3","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/JPROC.2023.3279374","volume":"111","author":"J Chen","year":"2023","unstructured":"Chen, J., et al.: Zero-shot and few-shot learning with knowledge graphs: a comprehensive survey. Proc. IEEE 111(6), 653\u2013685 (2023)","journal-title":"Proc. IEEE"},{"key":"33_CR4","doi-asserted-by":"publisher","first-page":"8225","DOI":"10.1109\/TMM.2022.3233442","volume":"25","author":"H Cheng","year":"2023","unstructured":"Cheng, H., Zhou, J.T., Tay, W.P., Wen, B.: Graph neural networks with triple attention for few-shot learning. IEEE Trans. Multimedia 25, 8225\u20138239 (2023)","journal-title":"IEEE Trans. Multimedia"},{"issue":"70","key":"33_CR5","first-page":"1","volume":"25","author":"HW Chung","year":"2024","unstructured":"Chung, H.W., et al.: Scaling instruction-finetuned language models. JMLR 25(70), 1\u201353 (2024)","journal-title":"JMLR"},{"key":"33_CR6","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Ding, K., Wang, J., Li, J., Shu, K., Liu, C., Liu, H.: Graph prototypical networks for few-shot learning on attributed networks. In: CIKM, pp. 295\u2013304 (2020)","DOI":"10.1145\/3340531.3411922"},{"key":"33_CR8","unstructured":"Fifty, C., Leskovec, J., Thrun, S.: In-context learning for few-shot molecular property prediction. CoRR arxiv:2310.08863 (2023)"},{"issue":"12","key":"33_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3659943","volume":"56","author":"H Gharoun","year":"2024","unstructured":"Gharoun, H., Momenifar, F., Chen, F., Gandomi, A.H.: Meta-learning approaches for few-shot learning: a survey of recent advances. ACM Comput. Surv. 56(12), 1\u201341 (2024)","journal-title":"ACM Comput. Surv."},{"key":"33_CR10","unstructured":"GLM, T., et al.: Chatglm: a family of large language models from glm-130b to glm-4 all tools (2024)"},{"key":"33_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2025.102505","volume":"121","author":"W Gong","year":"2025","unstructured":"Gong, W., et al.: Cgnet: few-shot learning for intracranial hemorrhage segmentation. Comput. Medical Imaging Graph. 121, 102505 (2025)","journal-title":"Comput. Medical Imaging Graph."},{"key":"33_CR12","unstructured":"Grattafiori, A., et\u00a0al.: The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)"},{"key":"33_CR13","unstructured":"He, P., Gao, J., Chen, W.: Debertav3: improving deberta using electra-style pre-training with gradient-disentangled embedding sharing (2021)"},{"key":"33_CR14","unstructured":"He, P., Liu, X., Gao, J., Chen, W.: Deberta: decoding-enhanced bert with disentangled attention. In: ICLR (2021)"},{"key":"33_CR15","unstructured":"Huang, K., Zitnik, M.: Graph meta learning via local subgraphs. In: NeurIPS 2020 (2020)"},{"key":"33_CR16","unstructured":"Huang, Q., et al.: PRODIGY: enabling in-context learning over graphs. In: NeurIPS (2023)"},{"key":"33_CR17","unstructured":"Ju, W., et al.: A survey of data-efficient graph learning. In: IJCAI, pp. 8104\u20138113 (2024)"},{"issue":"13","key":"33_CR18","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521\u20133526 (2017)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"33_CR19","unstructured":"Lang, S., et\u00a0al.: Aifs-ecmwf\u2019s data-driven forecasting system. arXiv preprint arXiv:2406.01465 (2024)"},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: ACL (2019)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y., Moghadam, P., Peng, C., Ye, N., Koniusz, P.: Inductive graph few-shot class incremental learning. In: WSDM, pp. 466\u2013474 (2025)","DOI":"10.1145\/3701551.3703578"},{"key":"33_CR22","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach (2019). arxiv:1907.11692"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: A simple but effective approach for unsupervised few-shot graph classification. In: WWW, pp. 4249\u20134259 (2024)","DOI":"10.1145\/3589334.3645587"},{"key":"33_CR24","unstructured":"Liu, Y., Ding, S., Zhou, S., Fan, W., Tan, Q.: Moleculargpt: open large language model (llm) for few-shot molecular property prediction. arXiv preprint arXiv:2406.12950 (2024)"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Lu, B., Gan, X., Yang, L., Zhang, W., Fu, L., Wang, X.: Geometer: graph few-shot class-incremental learning via prototype representation. In: SIGKDD, pp. 1152\u20131161 (2022)","DOI":"10.1145\/3534678.3539280"},{"key":"33_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2024.112031","volume":"212","author":"G Lu","year":"2024","unstructured":"Lu, G., Ju, X., Chen, X., Pei, W., Cai, Z.: GRACE: empowering llm-based software vulnerability detection with graph structure and in-context learning. J. Syst. Softw. 212, 112031 (2024)","journal-title":"J. Syst. Softw."},{"issue":"5594","key":"33_CR27","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1126\/science.298.5594.824","volume":"298","author":"R Milo","year":"2002","unstructured":"Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824\u2013827 (2002). https:\/\/doi.org\/10.1126\/science.298.5594.824","journal-title":"Science"},{"key":"33_CR28","unstructured":"MiniMax: Minimax-m1: Scaling test-time compute efficiently with lightning attention (2025). https:\/\/arxiv.org\/abs\/2506.13585"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Mosbach, M., Pimentel, T., Ravfogel, S., Klakow, D., Elazar, Y.: Few-shot fine-tuning vs. in-context learning: a fair comparison and evaluation. In: ACL, pp. 12284\u201312314 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.779"},{"key":"33_CR30","doi-asserted-by":"crossref","unstructured":"Pan, F., Wu, X., Li, Z., Luu, A.T.: Are LLMs good zero-shot fallacy classifiers? In: EMNLP, pp. 14338\u201314364 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.794"},{"key":"33_CR31","unstructured":"Paszke, A.: Pytorch: an imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703 (2019)"},{"key":"33_CR32","doi-asserted-by":"crossref","unstructured":"Rakaraddi, A., Siew-Kei, L., Pratama, M., de\u00a0Carvalho, M.V.: Graph mining under data scarcity. IJCNN 1\u20137 (2024)","DOI":"10.1109\/IJCNN60899.2024.10651061"},{"key":"33_CR33","unstructured":"Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. In: NeurIPS, pp. 4077\u20134087 (2017)"},{"issue":"13s","key":"33_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3582688","volume":"55","author":"Y Song","year":"2023","unstructured":"Song, Y., Wang, T., Cai, P., Mondal, S.K., Sahoo, J.P.: A comprehensive survey of few-shot learning: evolution, applications, challenges, and opportunities. ACM Comput. Surv. 55(13s), 1\u201340 (2023)","journal-title":"ACM Comput. Surv."},{"key":"33_CR35","unstructured":"Touvron, H., et\u00a0al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"key":"33_CR36","doi-asserted-by":"crossref","unstructured":"Wang, D., Zuo, Y., Li, F., Wu, J.: Llms as zero-shot graph learners: alignment of gnn representations with llm token embeddings. In: NeurIPS, vol.\u00a037, pp. 5950\u20135973 (2024)","DOI":"10.52202\/079017-0193"},{"key":"33_CR37","doi-asserted-by":"crossref","unstructured":"Wei, Y., Huang, Q., Zhang, Y., Kwok, J.T.: KICGPT: large language model with knowledge in context for knowledge graph completion. In: EMNLP, pp. 8667\u20138683 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.580"},{"key":"33_CR38","doi-asserted-by":"crossref","unstructured":"Wolf, T., et\u00a0al.: Transformers: state-of-the-art natural language processing. In: EMNLP, pp. 38\u201345 (2020)","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"33_CR39","unstructured":"Xu, J., Wu, Z., Lin, M., Zhang, X., Wang, S.: Llm and gnn are complementary: distilling llm for multimodal graph learning. arXiv preprint arXiv:2406.01032 (2024)"},{"key":"33_CR40","unstructured":"Yan, H., et al.: A comprehensive study on text-attributed graphs: benchmarking and rethinking. In: NeurIPS, vol.\u00a036, pp. 17238\u201317264 (2023)"},{"key":"33_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112845","volume":"310","author":"J Yang","year":"2025","unstructured":"Yang, J., Dong, Y., Li, G.: An adaptive dual-channel multi-modal graph neural network for few-shot learning. Knowl. Based Syst. 310, 112845 (2025)","journal-title":"Knowl. Based Syst."},{"key":"33_CR42","doi-asserted-by":"crossref","unstructured":"Yao, B., et al.: More samples or more prompts? Exploring effective few-shot in-context learning for llms with in-context sampling. In: NAACL, pp. 1772\u20131790 (2024)","DOI":"10.18653\/v1\/2024.findings-naacl.115"},{"key":"33_CR43","unstructured":"Ying, C., et al.: Do transformers really perform bad for graph representation? CoRR arxiv:2106.05234 (2021)"},{"key":"33_CR44","doi-asserted-by":"crossref","unstructured":"Yu, J., Ren, Y., Gong, C., Tan, J., Li, X., Zhang, X.: Leveraging large language models for node generation in few-shot learning on text-attributed graphs. In: AAAI, vol.\u00a039, pp. 13087\u201313095 (2025)","DOI":"10.1609\/aaai.v39i12.33428"},{"issue":"4","key":"33_CR45","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1109\/TKDE.2024.3523573","volume":"37","author":"M Yu","year":"2025","unstructured":"Yu, M., Zhang, Y., Sun, J., Huang, M., Zhang, T., Yu, G.: Hg-scc: a subgraph-aware convolutional few-shot classification method on heterogeneous graphs. IEEE Trans. Knowl. Data Eng. 37(4), 1871\u20131884 (2025)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"33_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128970","volume":"617","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y., Zhou, X., Wang, N., Tang, J., Xuan, T.: Doun-gnn: double nodes graph neural network for few-shot learning. Neurocomputing 617, 128970 (2025)","journal-title":"Neurocomputing"},{"key":"33_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, H., et al.: Pre-training and prompting for few-shot node classification on text-attributed graphs. In: KDD, pp. 4467\u20134478 (2024)","DOI":"10.1145\/3637528.3671952"},{"key":"33_CR48","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Graphclip: enhancing transferability in graph foundation models for text-attributed graphs. In: WWW, pp. 2183\u20132197 (2025)","DOI":"10.1145\/3696410.3714801"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5719-6_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:34:24Z","timestamp":1769718864000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5719-6_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819557189","9789819557196"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5719-6_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb2025.sau.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}