{"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/all-in-one-multi-task-prompting-for-graph-1","title":"All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)","arxiv_id":"2403.07040","date":"2024-03-11","proceeding":null,"authors":["Xiangguo Sun","Hong Cheng","Jia Li","Bo Liu","Jihong Guan"],"abstract":"This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan. All in one: Multi-task prompting for graph neural networks. KDD 23) The paper introduces a novel approach to bridging the gap between pre-trained graph models and the diverse tasks they're applied to, inspired by the success of prompt learning in NLP. Recognizing the challenge of aligning pre-trained models with varied graph tasks (node level, edge level, and graph level), which can lead to negative transfer and poor performance, we propose a multi-task prompting method for graphs. This method involves unifying graph and language prompt formats, enabling NLP's prompting strategies to be adapted for graph tasks. By analyzing the task space of graph applications, we reformulate problems to fit graph-level tasks and apply meta-learning to improve prompt initialization for multiple tasks. Experiments show our method's effectiveness in enhancing model performance across different graph tasks. Beyond the original work, in this extended abstract, we further discuss the graph prompt from a bigger picture and provide some of the latest work toward this area.","url_abs":"https://arxiv.org/abs/2403.07040v1","url_pdf":"https://arxiv.org/pdf/2403.07040v1.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":[],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"prompt-learning","task_name":"Prompt Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2403.07040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07040"}},"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":"deterministic:regex_extraction","url":"https://github.com/sheldonresearch/ProG","reach":null}],"summary":{"ran_draft_wrong":1,"ran":4},"by_repo_kind":{"found_in_text":{"samples":5,"ran":5,"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":0,"samples":[{"code_sha256_prefix":"3dac3e96855f63bd","entry":"averageemb","repo":"sheldonresearch/ProG","repo_kind":"found_in_text","path":"prompt_graph/prompt/MultiGprompt.py","file_url":"https://github.com/sheldonresearch/ProG/blob/HEAD/prompt_graph/prompt/MultiGprompt.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3dac3e96855f63bd"}},{"code_sha256_prefix":"d6d5e15009f0236f","entry":"downprompt","repo":"sheldonresearch/ProG","repo_kind":"found_in_text","path":"prompt_graph/prompt/MultiGprompt.py","file_url":"https://github.com/sheldonresearch/ProG/blob/HEAD/prompt_graph/prompt/MultiGprompt.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d6d5e15009f0236f"}},{"code_sha256_prefix":"6a4f6577ca85e535","entry":"downstreamprompt","repo":"sheldonresearch/ProG","repo_kind":"found_in_text","path":"prompt_graph/prompt/MultiGprompt.py","file_url":"https://github.com/sheldonresearch/ProG/blob/HEAD/prompt_graph/prompt/MultiGprompt.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6a4f6577ca85e535"}},{"code_sha256_prefix":"ef4309c038c1da38","entry":"weighted_feature","repo":"sheldonresearch/ProG","repo_kind":"found_in_text","path":"prompt_graph/prompt/MultiGprompt.py","file_url":"https://github.com/sheldonresearch/ProG/blob/HEAD/prompt_graph/prompt/MultiGprompt.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ef4309c038c1da38"}},{"code_sha256_prefix":"9500ee66e9aca23d","entry":"weighted_prompt","repo":"sheldonresearch/ProG","repo_kind":"found_in_text","path":"prompt_graph/prompt/MultiGprompt.py","file_url":"https://github.com/sheldonresearch/ProG/blob/HEAD/prompt_graph/prompt/MultiGprompt.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9500ee66e9aca23d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}