Evidence map›Paper›PMID 41678307›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

Strategy evolution on temporal hypergraphs.

Xiaochen Wang, Lei Zhou, Alex McAvoy, Zhenglong Tian, Aming Li

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xiaochen Wang *Center for Systems and Control, School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China.ORCID 0000-0003-2987-1312
Lei Zhou *School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Alex McAvoySchool of Data Science and Society, University of North Carolina, Chapel Hill, NC 27599.
Zhenglong TianCenter for Systems and Control, School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China.
Aming LiCenter for Systems and Control, School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China.ORCID 0000-0003-4045-8721

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Individuals interact and cooperate in structured systems. Many studies represent this structure using static networks, where each link represents a permanent connection between two nodes. However, real interactions are generally not time-invariant and are often not pairwise. Recently, progress has been made in modeling higher-order interactions using hypergraphs, where a link may connect more than two individuals. Here, we study cooperation on temporal hypergraphs, capturing the time-varying, higher-order interactions seen in empirical systems. We find that temporal hypergraphs can promote cooperation compared with static networks, and the latter may even underestimate the cooperation-boosting effects of constrained, local interactions. We further show that cooperation can be facilitated by temporal hypergraphs with sparse components and higher-order interactions. Importantly, when the size of group interactions (hyperedges) is comparable to the population size, relatively small hyperedge sizes best facilitate cooperation. Synthetic and empirical hypergraphs alike affirm our findings, illuminating how temporal, higher-order interactions profoundly shape the evolution of cooperation.

Indexed as

evolutionary game theoryevolution of cooperationnetwork reciprocitytemporal hypergraph

Identifiers

PMID41678307
PMCPMC12912996

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.