{"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/ned-an-inter-graph-node-metric-based-on-edit","title":"NED: An Inter-Graph Node Metric Based On Edit Distance","arxiv_id":"1602.02358","date":"2016-02-07","proceeding":null,"authors":["Haohan Zhu","Xianrui Meng","George Kollios"],"abstract":"Node similarity is a fundamental problem in graph analytics. However, node\nsimilarity between nodes in different graphs (inter-graph nodes) has not\nreceived a lot of attention yet. The inter-graph node similarity is important\nin learning a new graph based on the knowledge of an existing graph (transfer\nlearning on graphs) and has applications in biological, communication, and\nsocial networks. In this paper, we propose a novel distance function for\nmeasuring inter-graph node similarity with edit distance, called NED. In NED,\ntwo nodes are compared according to their local neighborhood structures which\nare represented as unordered k-adjacent trees, without relying on labels or\nother assumptions. Since the computation problem of tree edit distance on\nunordered trees is NP-Complete, we propose a modified tree edit distance,\ncalled TED*, for comparing neighborhood trees. TED* is a metric distance, as\nthe original tree edit distance, but more importantly, TED* is polynomially\ncomputable. As a metric distance, NED admits efficient indexing, provides\ninterpretable results, and shows to perform better than existing approaches on\na number of data analysis tasks, including graph de-anonymization. Finally, the\nefficiency and effectiveness of NED are empirically demonstrated using\nreal-world graphs.","url_abs":"http://arxiv.org/abs/1602.02358v3","url_pdf":"http://arxiv.org/pdf/1602.02358v3.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":"ned-an-inter-graph-node-metric-based-on-edit","repo_url":"https://github.com/zhuhaohan/NED","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}