{"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/predicting-diffusion-reach-probabilities-via","title":"Predicting Diffusion Reach Probabilities via Representation Learning on Social Networks","arxiv_id":"1901.03829","date":"2019-01-12","proceeding":null,"authors":["Furkan Gursoy","Ahmet Onur Durahim"],"abstract":"Diffusion reach probability between two nodes on a network is defined as the\nprobability of a cascade originating from one node reaching to another node. An\ninfinite number of cascades would enable calculation of true diffusion reach\nprobabilities between any two nodes. However, there exists only a finite number\nof cascades and one usually has access only to a small portion of all available\ncascades. In this work, we addressed the problem of estimating diffusion reach\nprobabilities given only a limited number of cascades and partial information\nabout underlying network structure. Our proposed strategy employs node\nrepresentation learning to generate and feed node embeddings into machine\nlearning algorithms to create models that predict diffusion reach\nprobabilities. We provide experimental analysis using synthetically generated\ncascades on two real-world social networks. Results show that proposed method\nis superior to using values calculated from available cascades when the portion\nof cascades is small.","url_abs":"http://arxiv.org/abs/1901.03829v1","url_pdf":"http://arxiv.org/pdf/1901.03829v1.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":"predicting-diffusion-reach-probabilities-via","repo_url":"https://github.com/furkangursoy/RLforDiffPred","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation 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}