{"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/learning-the-mechanisms-of-network-growth","title":"Learning the mechanisms of network growth","arxiv_id":"2404.00793","date":"2024-03-31","proceeding":null,"authors":["Lourens Touwen","Doina Bucur","Remco van der Hofstad","Alessandro Garavaglia","Nelly Litvak"],"abstract":"We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness.","url_abs":"https://arxiv.org/abs/2404.00793v3","url_pdf":"https://arxiv.org/pdf/2404.00793v3.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":"learning-the-mechanisms-of-network-growth","repo_url":"https://github.com/LourensT/DynamicNetworkSimulation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[{"slug":"synthetic-dynamic-networks","name":"Synthetic Dynamic Networks","full_name":"from Aging, Fitness Preferential Attachment mechanisms"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-classification-on-synthetic-dynamic","task":"Graph Classification","dataset":"Synthetic Dynamic Networks","model":"Time-cohort Dynamic Features + Static Features","rank_in_archive_order":1,"of":3,"metrics":{"Accuracy":"98.4"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-synthetic-dynamic","task":"Graph Classification","dataset":"Synthetic Dynamic Networks","model":"Size-cohort Dynamic Features + Static Features","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"98.06"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-synthetic-dynamic","task":"Graph Classification","dataset":"Synthetic Dynamic Networks","model":"Static Features","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"92.81%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}