{"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/coevolve-a-joint-point-process-model-for","title":"COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution","arxiv_id":"1507.02293","date":"2015-07-08","proceeding":"NeurIPS 2015 12","authors":["Mehrdad Farajtabar","Yichen Wang","Manuel Gomez Rodriguez","Shuang Li","Hongyuan Zha","Le Song"],"abstract":"Information diffusion in online social networks is affected by the underlying\nnetwork topology, but it also has the power to change it. Online users are\nconstantly creating new links when exposed to new information sources, and in\nturn these links are alternating the way information spreads. However, these\ntwo highly intertwined stochastic processes, information diffusion and network\nevolution, have been predominantly studied separately, ignoring their\nco-evolutionary dynamics.\n  We propose a temporal point process model, COEVOLVE, for such joint dynamics,\nallowing the intensity of one process to be modulated by that of the other.\nThis model allows us to efficiently simulate interleaved diffusion and network\nevents, and generate traces obeying common diffusion and network patterns\nobserved in real-world networks. Furthermore, we also develop a convex\noptimization framework to learn the parameters of the model from historical\ndiffusion and network evolution traces. We experimented with both synthetic\ndata and data gathered from Twitter, and show that our model provides a good\nfit to the data as well as more accurate predictions than alternatives.","url_abs":"http://arxiv.org/abs/1507.02293v2","url_pdf":"http://arxiv.org/pdf/1507.02293v2.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":"coevolve-a-joint-point-process-model-for","repo_url":"https://github.com/farajtabar/Coevolution","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1507.02293","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}