{"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/subgraph2vec-learning-distributed","title":"subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs","arxiv_id":"1606.08928","date":"2016-06-29","proceeding":null,"authors":["Annamalai Narayanan","Mahinthan Chandramohan","Lihui Chen","Yang Liu","Santhoshkumar Saminathan"],"abstract":"In this paper, we present subgraph2vec, a novel approach for learning latent\nrepresentations of rooted subgraphs from large graphs inspired by recent\nadvancements in Deep Learning and Graph Kernels. These latent representations\nencode semantic substructure dependencies in a continuous vector space, which\nis easily exploited by statistical models for tasks such as graph\nclassification, clustering, link prediction and community detection.\nsubgraph2vec leverages on local information obtained from neighbourhoods of\nnodes to learn their latent representations in an unsupervised fashion. We\ndemonstrate that subgraph vectors learnt by our approach could be used in\nconjunction with classifiers such as CNNs, SVMs and relational data clustering\nalgorithms to achieve significantly superior accuracies. Also, we show that the\nsubgraph vectors could be used for building a deep learning variant of\nWeisfeiler-Lehman graph kernel. Our experiments on several benchmark and\nlarge-scale real-world datasets reveal that subgraph2vec achieves significant\nimprovements in accuracies over existing graph kernels on both supervised and\nunsupervised learning tasks. Specifically, on two realworld program analysis\ntasks, namely, code clone and malware detection, subgraph2vec outperforms\nstate-of-the-art kernels by more than 17% and 4%, respectively.","url_abs":"http://arxiv.org/abs/1606.08928v1","url_pdf":"http://arxiv.org/pdf/1606.08928v1.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":"subgraph2vec-learning-distributed","repo_url":"https://github.com/mldroid/subgraph2vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"subgraph2vec-learning-distributed","repo_url":"https://github.com/mldroid/subgraph2vec_gensim","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"subgraph2vec-learning-distributed","repo_url":"https://github.com/mldroid/subgraph2vec_tf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"malware-detection","task_name":"Malware Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1606.08928","atlas_url":"https://app.syntology.ai/?focus=1606.08928","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}