{"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/modelling-sparsity-heterogeneity-reciprocity","title":"Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data","arxiv_id":"1803.06070","date":"2018-03-16","proceeding":"NeurIPS 2018 12","authors":["Xenia Miscouridou","François Caron","Yee Whye Teh"],"abstract":"We propose a novel class of network models for temporal dyadic interaction\ndata. Our goal is to capture a number of important features often observed in\nsocial interactions: sparsity, degree heterogeneity, community structure and\nreciprocity. We propose a family of models based on self-exciting Hawkes point\nprocesses in which events depend on the history of the process. The key\ncomponent is the conditional intensity function of the Hawkes Process, which\ncaptures the fact that interactions may arise as a response to past\ninteractions (reciprocity), or due to shared interests between individuals\n(community structure). In order to capture the sparsity and degree\nheterogeneity, the base (non time dependent) part of the intensity function\nbuilds on compound random measures following Todeschini et al. (2016). We\nconduct experiments on a variety of real-world temporal interaction data and\nshow that the proposed model outperforms many competing approaches for link\nprediction, and leads to interpretable parameters.","url_abs":"http://arxiv.org/abs/1803.06070v2","url_pdf":"http://arxiv.org/pdf/1803.06070v2.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":"modelling-sparsity-heterogeneity-reciprocity","repo_url":"https://github.com/OxCSML-BayesNP/HawkesNetOC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.06070","atlas_url":"https://app.syntology.ai/?focus=1803.06070","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}