{"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/graph2vec-learning-distributed","title":"graph2vec: Learning Distributed Representations of Graphs","arxiv_id":"1707.05005","date":"2017-07-17","proceeding":null,"authors":["Annamalai Narayanan","Mahinthan Chandramohan","Rajasekar Venkatesan","Lihui Chen","Yang Liu","Shantanu Jaiswal"],"abstract":"Recent works on representation learning for graph structured data\npredominantly focus on learning distributed representations of graph\nsubstructures such as nodes and subgraphs. However, many graph analytics tasks\nsuch as graph classification and clustering require representing entire graphs\nas fixed length feature vectors. While the aforementioned approaches are\nnaturally unequipped to learn such representations, graph kernels remain as the\nmost effective way of obtaining them. However, these graph kernels use\nhandcrafted features (e.g., shortest paths, graphlets, etc.) and hence are\nhampered by problems such as poor generalization. To address this limitation,\nin this work, we propose a neural embedding framework named graph2vec to learn\ndata-driven distributed representations of arbitrary sized graphs. graph2vec's\nembeddings are learnt in an unsupervised manner and are task agnostic. Hence,\nthey could be used for any downstream task such as graph classification,\nclustering and even seeding supervised representation learning approaches. Our\nexperiments on several benchmark and large real-world datasets show that\ngraph2vec achieves significant improvements in classification and clustering\naccuracies over substructure representation learning approaches and are\ncompetitive with state-of-the-art graph kernels.","url_abs":"http://arxiv.org/abs/1707.05005v1","url_pdf":"http://arxiv.org/pdf/1707.05005v1.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":"graph2vec-learning-distributed","repo_url":"https://github.com/MLDroid/graph2vec_tf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"graph2vec-learning-distributed","repo_url":"https://github.com/benedekrozemberczki/graph2vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"graph2vec-learning-distributed","repo_url":"https://github.com/benedekrozemberczki/karateclub","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"graph2vec-learning-distributed","repo_url":"https://github.com/compnet/pang","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"graph2vec-learning-distributed","repo_url":"https://github.com/paulmorio/geo2dr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"graph2vec-learning-distributed","repo_url":"https://github.com/soumavaghosh/graph2vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"graph-matching","task_name":"Graph Matching"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-classification-on-mutag","task":"Graph Classification","dataset":"MUTAG","model":"graph2vec","rank_in_archive_order":70,"of":74,"metrics":{"Accuracy":"83.15% ± 9.25%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-nci1","task":"Graph Classification","dataset":"NCI1","model":"graph2vec","rank_in_archive_order":57,"of":69,"metrics":{"Accuracy":"73.22% ± 1.81%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-nci109","task":"Graph Classification","dataset":"NCI109","model":"Graph2Vec","rank_in_archive_order":30,"of":38,"metrics":{"Accuracy":"74.26"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-proteins","task":"Graph Classification","dataset":"PROTEINS","model":"graph2vec","rank_in_archive_order":89,"of":103,"metrics":{"Accuracy":"73.3% ± 2.05%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-ptc","task":"Graph Classification","dataset":"PTC","model":"graph2vec","rank_in_archive_order":33,"of":37,"metrics":{"Accuracy":"60.17% ± 6.86%"},"uses_additional_data":false},{"leaderboard":"/sota/malware-detection-on-android-malware-dataset","task":"Malware Detection","dataset":"Android Malware Dataset","model":"Graph2Vec","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"99.03"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.05005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.05005"}},"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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