{"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/link-prediction-based-on-graph-neural","title":"Link Prediction Based on Graph Neural Networks","arxiv_id":"1802.09691","date":"2018-02-27","proceeding":"NeurIPS 2018 12","authors":["Muhan Zhang","Yixin Chen"],"abstract":"Link prediction is a key problem for network-structured data. Link prediction\nheuristics use some score functions, such as common neighbors and Katz index,\nto measure the likelihood of links. They have obtained wide practical uses due\nto their simplicity, interpretability, and for some of them, scalability.\nHowever, every heuristic has a strong assumption on when two nodes are likely\nto link, which limits their effectiveness on networks where these assumptions\nfail. In this regard, a more reasonable way should be learning a suitable\nheuristic from a given network instead of using predefined ones. By extracting\na local subgraph around each target link, we aim to learn a function mapping\nthe subgraph patterns to link existence, thus automatically learning a\n`heuristic' that suits the current network. In this paper, we study this\nheuristic learning paradigm for link prediction. First, we develop a novel\n$\\gamma$-decaying heuristic theory. The theory unifies a wide range of\nheuristics in a single framework, and proves that all these heuristics can be\nwell approximated from local subgraphs. Our results show that local subgraphs\nreserve rich information related to link existence. Second, based on the\n$\\gamma$-decaying theory, we propose a new algorithm to learn heuristics from\nlocal subgraphs using a graph neural network (GNN). Its experimental results\nshow unprecedented performance, working consistently well on a wide range of\nproblems.","url_abs":"http://arxiv.org/abs/1802.09691v3","url_pdf":"http://arxiv.org/pdf/1802.09691v3.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":"link-prediction-based-on-graph-neural","repo_url":"https://github.com/muhanzhang/SEAL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"link-prediction-based-on-graph-neural","repo_url":"https://github.com/arianemarandon/linkpredconf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"link-prediction-based-on-graph-neural","repo_url":"https://github.com/facebookresearch/SEAL_OGB","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"link-prediction-based-on-graph-neural","repo_url":"https://github.com/fs302/EasyLink","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"link-prediction-based-on-graph-neural","repo_url":"https://github.com/fs302/EasyLink/blob/main/example/ogbl_ppa_ra.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"link-prediction-based-on-graph-neural","repo_url":"https://github.com/sreycodes/YLR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"link-prediction-based-on-graph-neural","repo_url":"https://github.com/venomouscyanide/S3GRL_OGB","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"link-prediction-based-on-graph-neural","repo_url":"https://github.com/venomouscyanide/s3grl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"link-prediction-based-on-graph-neural","repo_url":"https://github.com/venomouscyanide/s3grl_ogb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"link-prediction-based-on-graph-neural","repo_url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/seal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-usair","task":"Link Prediction","dataset":"USAir","model":"SEAL","rank_in_archive_order":1,"of":2,"metrics":{"AUC":"97.09"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.09691","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.09691"}},"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. 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