{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/empower-text-attributed-graphs-learning-with","title":"Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphs","arxiv_id":"2310.09872","date":"2023-10-15","proceeding":null,"authors":["Jianxiang Yu","Yuxiang Ren","Chenghua Gong","Jiaqi Tan","Xiang Li","Xuecang Zhang"],"abstract":"Text-attributed graphs have recently garnered significant attention due to their wide range of applications in web domains. Existing methodologies employ word embedding models for acquiring text representations as node features, which are subsequently fed into Graph Neural Networks (GNNs) for training. Recently, the advent of Large Language Models (LLMs) has introduced their powerful capabilities in information retrieval and text generation, which can greatly enhance the text attributes of graph data. Furthermore, the acquisition and labeling of extensive datasets are both costly and time-consuming endeavors. Consequently, few-shot learning has emerged as a crucial problem in the context of graph learning tasks. In order to tackle this challenge, we propose a lightweight paradigm called LLM4NG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs. Specifically, we utilize LLMs to extract semantic information from the labels and generate samples that belong to these categories as exemplars. Subsequently, we employ an edge predictor to capture the structural information inherent in the raw dataset and integrate the newly generated samples into the original graph. This approach harnesses LLMs for enhancing class-level information and seamlessly introduces labeled nodes and edges without modifying the raw dataset, thereby facilitating the node classification task in few-shot scenarios. Extensive experiments demonstrate the outstanding performance of our proposed paradigm, particularly in low-shot scenarios. For instance, in the 1-shot setting of the ogbn-arxiv dataset, LLM4NG achieves a 76% improvement over the baseline model.","url_abs":"https://arxiv.org/abs/2310.09872v2","url_pdf":"https://arxiv.org/pdf/2310.09872v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"empower-text-attributed-graphs-learning-with","repo_url":"https://github.com/jianxiangyu/llm4ng","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"graph-learning","task_name":"Graph Learning"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2310.09872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09872"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jianxiangyu/llm4ng","reach":{"status":"ok"}}],"summary":{"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":4,"samples":[{"code_sha256_prefix":"3a8cbab6af8e596e","entry":"get_cora_casestudy","repo":"jianxiangyu/llm4ng","repo_kind":"official","path":"load_cora.py","file_url":"https://github.com/jianxiangyu/llm4ng/blob/HEAD/load_cora.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"3a8cbab6af8e596e"}},{"code_sha256_prefix":"be2a002c61f5e17e","entry":"get_pubmed_casestudy","repo":"jianxiangyu/llm4ng","repo_kind":"official","path":"load_pubmed.py","file_url":"https://github.com/jianxiangyu/llm4ng/blob/HEAD/load_pubmed.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"be2a002c61f5e17e"}},{"code_sha256_prefix":"29aa1f2c77d5d79d","entry":"load_cora","repo":"jianxiangyu/llm4ng","repo_kind":"official","path":"load_cora.py","file_url":"https://github.com/jianxiangyu/llm4ng/blob/HEAD/load_cora.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"29aa1f2c77d5d79d"}},{"code_sha256_prefix":"3a81d8887bbd30cc","entry":"load_pubmed","repo":"jianxiangyu/llm4ng","repo_kind":"official","path":"load_pubmed.py","file_url":"https://github.com/jianxiangyu/llm4ng/blob/HEAD/load_pubmed.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"3a81d8887bbd30cc"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}