{"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/nodesketch-highly-efficient-graph-embeddings","title":"NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching","arxiv_id":null,"date":"2020-05-13","proceeding":"‏‏‎ ‎ 2020 5","authors":["Dingqi Yang","Paolo Rosso","Bin Li","Philippe Cudre-Mauroux"],"abstract":"Embeddings have become a key paradigm to learn graph represen-tations and facilitate downstream graph analysis tasks. Existinggraph embedding techniques either sample a large number of nodepairs from a graph to learn node embeddings via stochastic op-timization, or factorize a high-order proximity/adjacency matrixof the graph via expensive matrix factorization. However, thesetechniques usually require significant computational resources forthe learning process, which hinders their applications on large-scale graphs. Moreover, the cosine similarity preserved by thesetechniques shows suboptimal efficiency in downstream graph anal-ysis tasks, compared to Hamming similarity, for example. To ad-dress these issues, we propose NodeSketch, a highly-efficient graphembedding technique preserving high-order node proximity viarecursive sketching. Specifically, built on top of an efficient data-independent hashing/sketching technique, NodeSketch generatesnode embeddings in Hamming space. For an input graph, it starts bysketching the self-loop-augmented adjacency matrix of the graphto output low-order node embeddings, and then recursively gener-atesk-order node embeddings based on the self-loop-augmentedadjacency matrix and (k-1)-order node embeddings. Our extensiveevaluation compares NodeSketch against a sizable collection ofstate-of-the-art techniques using five real-world graphs on twograph analysis tasks. The results show that NodeSketch achievesstate-of-the-art performance compared to these techniques, whileshowing significant speedup of 9x-372x in the embedding learningprocess and 1.19x-1.68x speedup when performing downstreamgraph analysis tasks","url_abs":"https://exascale.info/assets/pdf/yang2019nodesketch.pdf","url_pdf":"https://exascale.info/assets/pdf/yang2019nodesketch.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":"nodesketch-highly-efficient-graph-embeddings","repo_url":"https://github.com/eXascaleInfolab/NodeSketch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"nodesketch-highly-efficient-graph-embeddings","repo_url":"https://github.com/benedekrozemberczki/karateclub","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}