{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T19:02:51Z","timestamp":1771614171372,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819541577","type":"print"},{"value":"9789819541584","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\/978-981-95-4158-4_7","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:32:31Z","timestamp":1767321151000},"page":"106-122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Large Language Models as\u00a0Topological Structure Enhancers for\u00a0Text-Attributed Graphs"],"prefix":"10.1007","author":[{"given":"Shengyin","family":"Sun","sequence":"first","affiliation":[]},{"given":"Yuxiang","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Jiehao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"7_CR1","unstructured":"Brown, T., et\u00a0al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems (2020)"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: Exploring the potential of large language models (llms) in learning on graphs. In: ACM SIGKDD Explorations Newsletter, vol.\u00a025 (2024)","DOI":"10.1145\/3655103.3655110"},{"key":"7_CR3","unstructured":"Chien, E., et al.: Node feature extraction by self-supervised multi-scale neighborhood prediction. In: Proc. Int. Conf. Learning Representations (2022)"},{"key":"7_CR4","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: NAACL (2023)"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Giles, C.L., Bollacker, K.D., Lawrence, S.: Citeseer: an automatic citation indexing system. In: Proc. ACM Conf. Digital Libraries (1998)","DOI":"10.1145\/276675.276685"},{"key":"7_CR6","unstructured":"Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems (2017)"},{"key":"7_CR7","unstructured":"He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. In: arXiv:2006.03654 (2020)"},{"key":"7_CR8","unstructured":"He, X., Bresson, X., Laurent, T., Perold, A., LeCun, Y., Hooi, B.: Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning. In: Proc. Int. Conf. Learning Representations (2024)"},{"key":"7_CR9","unstructured":"Hong, C., Liu, Z., Yang, J.: Sparsity induced similarity measure for label propagation. In: Proc. IEEE Int. Conf. Computer Vision (2009)"},{"key":"7_CR10","unstructured":"Hu, W., et al.: Open graph benchmark: Datasets for machine learning on graphs. arXiv:2005.00687 (2020)"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Hu, Y., Chen, C., Yang, C.H., Li, R., Zhang, D., Chen, Z., Chng, E.: Gentranslate: large language models are generative multilingual speech and machine translators. In: ACL (2024)","DOI":"10.18653\/v1\/2024.acl-long.5"},{"key":"7_CR12","unstructured":"Kaplan, J., McCandlish, S., et al.: Scaling laws for neural language models. arXiv:2001.08361 (2020)"},{"key":"7_CR13","unstructured":"Karasuyama, M., Karasuyama, H.: Manifold-based similarity adaptation for label propagation. In: Advances in Neural Information Processing Systems (2013)"},{"key":"7_CR14","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proc. Int. Conf. Learning Representations (2016)"},{"key":"7_CR15","unstructured":"Lin, X., Kang, J., Cong, W., Tong, H.: Bemap: balanced message passing for fair graph neural network. In: LoG (2023)"},{"key":"7_CR16","unstructured":"Liu, Y., et al.: Learning to propagate labels: transductive propagation network for few-shot learning. In: Proc. Int. Conf. Learning Representations (2019)"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Mai, Z., Zhang, J., Xu, Z., Xiao, Z.: Financial sentiment analysis meets llama 3: A comprehensive analysis. In: MLMI (2024)","DOI":"10.1145\/3696271.3696299"},{"key":"7_CR18","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1023\/A:1009953814988","volume":"3","author":"AK McCallum","year":"2000","unstructured":"McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Inf. Retrieval 3, 127\u2013163 (2000)","journal-title":"Inf. Retrieval"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Miaschi, A., Dell\u2019Orletta, F.: Contextual and non-contextual word embeddings: an in-depth linguistic investigation. In: ACL (2020)","DOI":"10.18653\/v1\/2020.repl4nlp-1.15"},{"key":"7_CR20","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013)"},{"key":"7_CR21","unstructured":"OpenAI: Gpt-4 technical report. arXiv:2303.08774 (2023)"},{"key":"7_CR22","unstructured":"Qin, Y., Wang, X., Zhang, Z., Zhu, W.: Disentangled representation learning with large language models for text-attributed graphs. arXiv:2310.18152 (2023)"},{"issue":"10","key":"7_CR23","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1007\/s11431-020-1647-3","volume":"63","author":"X Qiu","year":"2020","unstructured":"Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., Huang, X.: Pre-trained models for natural language processing: a survey. Sci. China Technol. Sci. 63(10), 1872\u20131897 (2020)","journal-title":"Sci. China Technol. Sci."},{"issue":"3","key":"7_CR24","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93\u2013106 (2008)","journal-title":"AI Mag."},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Sun, S., Ma, C.: Hyperbolic contrastive learning with model-augmentation for knowledge-aware recommendation. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (2024)","DOI":"10.1007\/978-3-031-70371-3_12"},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"Sun, S., et al.: Gdiffretro: retrosynthesis prediction with dual graph enhanced molecular representation and diffusion generation. arXiv:2501.08001 (2025)","DOI":"10.1609\/aaai.v39i12.33373"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Tang, J., et al.: Graphgpt: graph instruction tuning for large language models. arXiv:2310.13023 (2023)","DOI":"10.1145\/3626772.3657775"},{"key":"7_CR28","unstructured":"Touvron, H., et al.: Llama: open and efficient foundation language models. arXiv:2302.13971 (2023)"},{"key":"7_CR29","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: Proc. Int. Conf. Learning Representations (2018)"},{"issue":"4","key":"7_CR30","first-page":"1","volume":"40","author":"H Wang","year":"2021","unstructured":"Wang, H., Leskovec, J.: Combining graph convolutional neural networks and label propagation. ACM Trans. Inform. Syst. 40(4), 1\u201327 (2021)","journal-title":"ACM Trans. Inform. Syst."},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, R., Wang, M., Lai, T., Zhang, M.: Self-supervised transformer-based pre-training method with general plant infection dataset. In: PRCV (2024)","DOI":"10.1007\/978-981-97-8490-5_14"},{"key":"7_CR32","unstructured":"Xiao, Z., Blanco, E., Huang, Y.: Analyzing large language models\u2019 capability in location prediction. In: LREC-COLING (2024)"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Yang, M., Meng, Z., King, I.: L2 feature normalization for dynamic graph embedding. In: Proc. IEEE Int. Conf. Data Mining (2020)","DOI":"10.1109\/ICDM50108.2020.00082"},{"key":"7_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, H., et al.: Gimlet: a unified graph-text model for instruction-based molecule zero-shot learning. In: arXiv:2306.13089 (2023)","DOI":"10.1101\/2023.05.30.542904"},{"key":"7_CR35","unstructured":"Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Sch\u00f6lkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems (2004)"},{"key":"7_CR36","doi-asserted-by":"crossref","unstructured":"Zhou, D., Sch\u00f6lkopf, B.: Regularization on discrete spaces. In: DAGM Symposium (2005)","DOI":"10.1007\/11550518_45"},{"key":"7_CR37","unstructured":"Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proc. Int. Conf. Machine Learning (2003)"},{"key":"7_CR38","unstructured":"Zhu, Y., Wang, Y., Shi, H., Tang, S.: Efficient tuning and inference for large language models on textual graphs. arXiv:2401.15569 (2024)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4158-4_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:32:35Z","timestamp":1767321155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4158-4_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819541577","9789819541584"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4158-4_7","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":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"26 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2025.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}