{"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/line-large-scale-information-network","title":"LINE: Large-scale Information Network Embedding","arxiv_id":"1503.03578","date":"2015-03-12","proceeding":null,"authors":["Jian Tang","Meng Qu","Mingzhe Wang","Ming Zhang","Jun Yan","Qiaozhu Mei"],"abstract":"This paper studies the problem of embedding very large information networks\ninto low-dimensional vector spaces, which is useful in many tasks such as\nvisualization, node classification, and link prediction. Most existing graph\nembedding methods do not scale for real world information networks which\nusually contain millions of nodes. In this paper, we propose a novel network\nembedding method called the \"LINE,\" which is suitable for arbitrary types of\ninformation networks: undirected, directed, and/or weighted. The method\noptimizes a carefully designed objective function that preserves both the local\nand global network structures. An edge-sampling algorithm is proposed that\naddresses the limitation of the classical stochastic gradient descent and\nimproves both the effectiveness and the efficiency of the inference. Empirical\nexperiments prove the effectiveness of the LINE on a variety of real-world\ninformation networks, including language networks, social networks, and\ncitation networks. The algorithm is very efficient, which is able to learn the\nembedding of a network with millions of vertices and billions of edges in a few\nhours on a typical single machine. The source code of the LINE is available\nonline.","url_abs":"http://arxiv.org/abs/1503.03578v1","url_pdf":"http://arxiv.org/pdf/1503.03578v1.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":"line-large-scale-information-network","repo_url":"https://github.com/tangjianpku/LINE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"line-large-scale-information-network","repo_url":"https://github.com/2myeonggyu/Graph-Embedding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"line-large-scale-information-network","repo_url":"https://github.com/abhilash1910/Deep-Graph-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"line-large-scale-information-network","repo_url":"https://github.com/ink-usc/request","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"line-large-scale-information-network","repo_url":"https://github.com/leihuayi/NetworkEmbedding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"line-large-scale-information-network","repo_url":"https://github.com/liuxinkai94/Graph-embedding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"line-large-scale-information-network","repo_url":"https://github.com/ninoxjy/graph-embedding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"line-large-scale-information-network","repo_url":"https://github.com/shenweichen/GraphEmbedding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"line-large-scale-information-network","repo_url":"https://github.com/zxhhh97/ABot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"network-embedding","task_name":"Network Embedding"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[{"method_slug":"line","method_name":"LINE"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/node-classification-on-blogcatalog","task":"Node Classification","dataset":"Eximtradedata","model":"LINE","rank_in_archive_order":3,"of":5,"metrics":{"Accuracy":"20.50%","Macro-F1":"0.192"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-wikipedia","task":"Node Classification","dataset":"Wikipedia","model":"LINE","rank_in_archive_order":4,"of":6,"metrics":{"Accuracy":"17.50%","Macro-F1":"0.164"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1503.03578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1503.03578"}},"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/2myeonggyu/Graph-Embedding","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/shenweichen/GraphEmbedding","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/liuxinkai94/Graph-embedding","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/abhilash1910/Deep-Graph-Learning","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ink-usc/request","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zxhhh97/ABot","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ninoxjy/graph-embedding","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tangjianpku/LINE","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/leihuayi/NetworkEmbedding","reach":{"status":"ok"}}],"summary":{"unverified":3},"by_repo_kind":{"listed":{"samples":3,"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":0,"samples":[{"code_sha256_prefix":"639090b6f394e431","entry":"load_as_dict","repo":"ink-usc/request","repo_kind":"listed","path":"code/Evaluation/DataIO.py","file_url":"https://github.com/ink-usc/request/blob/HEAD/code/Evaluation/DataIO.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"639090b6f394e431"}},{"code_sha256_prefix":"d86d209ddd994d96","entry":"load_as_list","repo":"ink-usc/request","repo_kind":"listed","path":"code/Evaluation/DataIO.py","file_url":"https://github.com/ink-usc/request/blob/HEAD/code/Evaluation/DataIO.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d86d209ddd994d96"}},{"code_sha256_prefix":"ab6070a4d1da8d0a","entry":"load_map","repo":"ink-usc/request","repo_kind":"listed","path":"code/Evaluation/DataIO.py","file_url":"https://github.com/ink-usc/request/blob/HEAD/code/Evaluation/DataIO.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ab6070a4d1da8d0a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}