{"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/fast-sequence-based-embedding-with-diffusion","title":"Fast Sequence Based Embedding with Diffusion Graphs","arxiv_id":null,"date":"2018-03-20","proceeding":"CompleNet 2018 3","authors":["Benedek Rozemberczki","Rik Sarkar"],"abstract":"A graph embedding is a representation of the vertices of a graph in a low dimensional space, which approximately preserves proper-ties such as distances between nodes. Vertex sequence based embedding procedures  use  features  extracted  from  linear  sequences  of  vertices  to create  embeddings  using  a  neural  network.  In  this  paper,  we  propose diffusion  graphs  as  a  method  to  rapidly  generate  vertex  sequences  for network embedding. Its computational efficiency is superior to previous methods due to simpler sequence generation, and it produces more ac-curate results. In experiments, we found that the performance relative to  other  methods  improves  with  increasing  edge  density  in  the  graph.In a community detection task, clustering nodes in the embedding space produces  better  results  compared  to  other  sequence  based  embedding methods.","url_abs":"http://homepages.inf.ed.ac.uk/s1668259/papers/sequence.pdf","url_pdf":"http://homepages.inf.ed.ac.uk/s1668259/papers/sequence.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":"fast-sequence-based-embedding-with-diffusion","repo_url":"https://github.com/benedekrozemberczki/diff2vec","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"fast-sequence-based-embedding-with-diffusion","repo_url":"https://github.com/benedekrozemberczki/karateclub","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"network-embedding","task_name":"Network 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}