{"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/correlation-robust-influence-maximization","title":"Correlation Robust Influence Maximization","arxiv_id":"2010.14620","date":"2020-10-24","proceeding":"NeurIPS 2020 12","authors":["Louis Chen","Divya Padmanabhan","Chee Chin Lim","Karthik Natarajan"],"abstract":"We propose a distributionally robust model for the influence maximization problem. Unlike the classic independent cascade model \\citep{kempe2003maximizing}, this model's diffusion process is adversarially adapted to the choice of seed set. Hence, instead of optimizing under the assumption that all influence relationships in the network are independent, we seek a seed set whose expected influence under the worst correlation, i.e. the \"worst-case, expected influence\", is maximized. We show that this worst-case influence can be efficiently computed, and though the optimization is NP-hard, a ($1 - 1/e$) approximation guarantee holds. We also analyze the structure to the adversary's choice of diffusion process, and contrast with established models. Beyond the key computational advantages, we also highlight the extent to which the independence assumption may cost optimality, and provide insights from numerical experiments comparing the adversarial and independent cascade model.","url_abs":"https://arxiv.org/abs/2010.14620v2","url_pdf":"https://arxiv.org/pdf/2010.14620v2.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":"correlation-robust-influence-maximization","repo_url":"https://github.com/justanothergithubber/corr-im","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}