{"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/from-node-embedding-to-community-embedding","title":"From Node Embedding To Community Embedding","arxiv_id":"1610.09950","date":"2016-10-31","proceeding":null,"authors":["Vincent W. Zheng","Sandro Cavallari","Hongyun Cai","Kevin Chen-Chuan Chang","Erik Cambria"],"abstract":"Most of the existing graph embedding methods focus on nodes, which aim to\noutput a vector representation for each node in the graph such that two nodes\nbeing \"close\" on the graph are close too in the low-dimensional space. Despite\nthe success of embedding individual nodes for graph analytics, we notice that\nan important concept of embedding communities (i.e., groups of nodes) is\nmissing. Embedding communities is useful, not only for supporting various\ncommunity-level applications, but also to help preserve community structure in\ngraph embedding. In fact, we see community embedding as providing a\nhigher-order proximity to define the node closeness, whereas most of the\npopular graph embedding methods focus on first-order and/or second-order\nproximities. To learn the community embedding, we hinge upon the insight that\ncommunity embedding and node embedding reinforce with each other. As a result,\nwe propose ComEmbed, the first community embedding method, which jointly\noptimizes the community embedding and node embedding together. We evaluate\nComEmbed on real-world data sets. We show it outperforms the state-of-the-art\nbaselines in both tasks of node classification and community prediction.","url_abs":"http://arxiv.org/abs/1610.09950v2","url_pdf":"http://arxiv.org/pdf/1610.09950v2.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":"from-node-embedding-to-community-embedding","repo_url":"https://github.com/andompesta/nodeembedding-to-communityembedding","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"from-node-embedding-to-community-embedding","repo_url":"https://github.com/vwz/topolstm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}