{"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/distributed-representation-of-subgraphs","title":"Distributed Representation of Subgraphs","arxiv_id":"1702.06921","date":"2017-02-22","proceeding":null,"authors":["Bijaya Adhikari","Yao Zhang","Naren Ramakrishnan","B. Aditya Prakash"],"abstract":"Network embeddings have become very popular in learning effective feature\nrepresentations of networks. Motivated by the recent successes of embeddings in\nnatural language processing, researchers have tried to find network embeddings\nin order to exploit machine learning algorithms for mining tasks like node\nclassification and edge prediction. However, most of the work focuses on\nfinding distributed representations of nodes, which are inherently ill-suited\nto tasks such as community detection which are intuitively dependent on\nsubgraphs.\n  Here, we propose sub2vec, an unsupervised scalable algorithm to learn feature\nrepresentations of arbitrary subgraphs. We provide means to characterize\nsimilarties between subgraphs and provide theoretical analysis of sub2vec and\ndemonstrate that it preserves the so-called local proximity. We also highlight\nthe usability of sub2vec by leveraging it for network mining tasks, like\ncommunity detection. We show that sub2vec gets significant gains over\nstate-of-the-art methods and node-embedding methods. In particular, sub2vec\noffers an approach to generate a richer vocabulary of features of subgraphs to\nsupport representation and reasoning.","url_abs":"http://arxiv.org/abs/1702.06921v1","url_pdf":"http://arxiv.org/pdf/1702.06921v1.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":"community-detection","task_name":"Community Detection"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/malware-detection-on-android-malware-dataset","task":"Malware Detection","dataset":"Android Malware Dataset","model":"sub2vec","rank_in_archive_order":4,"of":4,"metrics":{"Accuracy":"76.83"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}