{"paper":{"title":"Event-based spatiotemporal networks for modelling emergent phenomena in complex systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Event-based spatiotemporal networks generate emergent behaviors in complex systems by encoding processes as discrete space-time events.","cross_cats":["cond-mat.dis-nn"],"primary_cat":"physics.soc-ph","authors_text":"Carl D. Modes, Debabrata Panja, Francesco Corman, Matthijs Romeijnders, Michiel van Boven, Phillip Staniczencko","submitted_at":"2026-05-15T09:54:39Z","abstract_excerpt":"Complex systems display emergent phenomena that vary significantly across spatial and temporal scales. These variations originate from fine-grained system processes, yet arriving at macroscopic dynamics from micro-level data -- particularly when large, high-resolution datasets are available -- remains a persistent challenge. Here we develop event-based spatiotemporal networks, a computational modelling framework that encodes system processes as discrete events anchored in space and time. Event-based spatiotemporal networks offer a unified, flexible and efficient approach to generate emergent b"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Event-based spatiotemporal networks offer a unified, flexible and efficient approach to generate emergent behaviour in complex systems across space and time from these events.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That real-world system processes can be adequately represented as discrete events anchored in space and time without critical loss of information or introduction of artifacts that distort emergent dynamics.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Event-based spatiotemporal networks encode micro-level processes as discrete space-time events to generate and analyze emergent macroscopic dynamics in complex systems.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Event-based spatiotemporal networks generate emergent behaviors in complex systems by encoding processes as discrete space-time events.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"909a428f548d64ae3267239a44af657dc42dccc9bb9998f63144be0938bbb328"},"source":{"id":"2605.15798","kind":"arxiv","version":1},"verdict":{"id":"5729c74d-badc-4340-ab25-9cf5331f71f5","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T19:51:46.707966Z","strongest_claim":"Event-based spatiotemporal networks offer a unified, flexible and efficient approach to generate emergent behaviour in complex systems across space and time from these events.","one_line_summary":"Event-based spatiotemporal networks encode micro-level processes as discrete space-time events to generate and analyze emergent macroscopic dynamics in complex systems.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That real-world system processes can be adequately represented as discrete events anchored in space and time without critical loss of information or introduction of artifacts that distort emergent dynamics.","pith_extraction_headline":"Event-based spatiotemporal networks generate emergent behaviors in complex systems by encoding processes as discrete space-time events."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15798/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T20:01:19.144506Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:01:15.010287Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:48.740037Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:21:55.904245Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"0ffe4ac826686862806362964c5c46c6d3f967c329f6179000821c865ee04d42"},"references":{"count":67,"sample":[{"doi":"","year":2023,"title":"L. Ambühl, M. Menendez, M. C. González, Understanding congestion propagation by combining percolation theory with the macroscopic fundamental diagram,Commun. Phys.6, 26 (2023)","work_id":"fdab0542-9f9c-49cc-91b1-726f93dd7748","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"M. U. G. Kraemeret al, Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence, Science373, 889 (2021)","work_id":"8ea867fb-3e1e-409d-97eb-674d3b954bc3","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2012,"title":"T. Viscek, A. Zafeiris, Collective motion,Phys. Rep.517, 71 (2012)","work_id":"e43f462f-3eb5-49ac-a62c-09568e93800e","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2013,"title":"M. G. Saunders, G. A. Voth, Coarse-graining methods for computational biology,Annual Rev. 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