Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes
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Abstract (arXiv)
Higher-order networks have emerged as a powerful framework to model complex systems and their collective behavior. Going beyond pairwise interactions, they encode structured relations among arbitrary numbers of units through representations such as simplicial complexes and hypergraphs. So far, the choice between simplicial complexes and hypergraphs has often been motivated by technical convenience. Here, using synchronization as an example, we demonstrate that the effects of higher-order interactions are highly representation-dependent. In particular, higher-order interactions typically enhance synchronization in hypergraphs but have the opposite effect in simplicial complexes. We provide theoretical insight by linking the synchronizability of different hypergraph structures to (generalized) degree heterogeneity and cross-order degree correlation, which in turn influence a wide range of dynamical processes from contagion to diffusion. Our findings reveal the hidden impact of higher-order representations on collective dynamics, highlighting the importance of choosing appropriate representations when studying systems with nonpairwise interactions.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2022.
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Citing papers per year: 2023: 3; 2024: 2; 2025: 5; 2026: 2.
12 papers.
- 2023CAT-Walk: Inductive Hypergraph Learning via Set Walkscited by 6
- 2024Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamicscited by 5
- 2023Topological Data Analysis in smart manufacturing: State of the art and futuredirectionscited by 1
- 2025Higher-Order Kuramoto Oscillator Network for Dense Associative Memorycited by 1
- 2026SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometrycited by 1
- 2023XFlow: Benchmarking Flow Behaviors over Graphscited by 0
- 2024Exploring higher-order neural network node interactions with total correlationcited by 0
- 2025Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Modelscited by 0
- 2025DCHO: A Decomposition-Composition Framework for Predicting Higher-Order Brain Connectivity to Enhance Diverse Downstream Applicationscited by 0
- 2025Learning Multi-Order Block Structure in Higher-Order Networkscited by 0
- 2025Unlocking the Invisible Urban Traffic Dynamics under Extreme Weather: A New Physics-Constrained Hamiltonian Learning Algorithmcited by 0
- 2026HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddingscited by 0
- 2026HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings16 May 2026
- 2026SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry1 Mar 2026
- 2025Unlocking the Invisible Urban Traffic Dynamics under Extreme Weather: A New Physics-Constrained Hamiltonian Learning Algorithm3 Dec 2025
- 2025Learning Multi-Order Block Structure in Higher-Order Networks26 Nov 2025
- 2025DCHO: A Decomposition-Composition Framework for Predicting Higher-Order Brain Connectivity to Enhance Diverse Downstream Applications27 Aug 2025
- 2025Higher-Order Kuramoto Oscillator Network for Dense Associative Memory29 Jul 2025
- 2025Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models28 May 2025
- 2024Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics18 Nov 2024
- 2024Exploring higher-order neural network node interactions with total correlation6 Feb 2024
- 2023Topological Data Analysis in smart manufacturing: State of the art and futuredirections13 Oct 2023
- 2023XFlow: Benchmarking Flow Behaviors over Graphs7 Aug 2023
- 2023CAT-Walk: Inductive Hypergraph Learning via Set Walks19 Jun 2023