Pith Number

pith:CJ5O3NMR

pith:2026:CJ5O3NMR2P3QWBVAVXCWTWD7RS

not attested not anchored not stored refs resolved

Event-based spatiotemporal networks for modelling emergent phenomena in complex systems

Carl D. Modes, Debabrata Panja, Francesco Corman, Matthijs Romeijnders, Michiel van Boven, Phillip Staniczencko

Event-based spatiotemporal networks generate emergent behaviors in complex systems by encoding processes as discrete space-time events.

arxiv:2605.15798 v1 · 2026-05-15 · physics.soc-ph · cond-mat.dis-nn

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\usepackage{pith}
\pithnumber{CJ5O3NMR2P3QWBVAVXCWTWD7RS}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest 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.

C2weakest 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.

C3one 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.

References

67 extracted · 67 resolved · 0 Pith anchors

[1] L. Ambühl, M. Menendez, M. C. González, Understanding congestion propagation by combining percolation theory with the macroscopic fundamental diagram,Commun. Phys.6, 26 (2023) 2023

[2] M. U. G. Kraemeret al, Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence, Science373, 889 (2021) 2021

[3] T. Viscek, A. Zafeiris, Collective motion,Phys. Rep.517, 71 (2012) 2012

[4] M. G. Saunders, G. A. Voth, Coarse-graining methods for computational biology,Annual Rev. Biophys.42, 73 (2013) 2013

[5] H. R. Wilson, J. D. Cowan, Excitatory and inhibitory interactions in localized populations of model neurons,Biophys. J.12, 1 (1972). 13 1972

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:18.904355Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

127aedb591d3f70b06a0adc569d87f8c99d8657e2f12dcdaf15c0a07ffbf2de8

Aliases

arxiv: 2605.15798 · arxiv_version: 2605.15798v1 · doi: 10.48550/arxiv.2605.15798 · pith_short_12: CJ5O3NMR2P3Q · pith_short_16: CJ5O3NMR2P3QWBVA · pith_short_8: CJ5O3NMR

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CJ5O3NMR2P3QWBVAVXCWTWD7RS \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 127aedb591d3f70b06a0adc569d87f8c99d8657e2f12dcdaf15c0a07ffbf2de8

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "0cb8db308e19352d95ba78bbbd868e5ee359aae0fa05de7c2b4f65e54dc21867",
    "cross_cats_sorted": [
      "cond-mat.dis-nn"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "physics.soc-ph",
    "submitted_at": "2026-05-15T09:54:39Z",
    "title_canon_sha256": "2f235ab0feb297c712d23e96e50f7e7fe88e6ec644a651f281c9b7e68242afff"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15798",
    "kind": "arxiv",
    "version": 1
  }
}