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Analyzing states beyond full synchronization on hypergraphs requires methods beyond projected networks

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arxiv 2107.13712 v1 pith:UQMIML6P submitted 2021-07-29 nlin.AO cond-mat.dis-nnnlin.CD

classification nlin.AOcond-mat.dis-nnnlin.CD
keywords hypergraphhypergraphsprojectedstructuresynchronizationanalyzingclusterdynamics
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A common approach for analyzing hypergraphs is to consider the projected adjacency or Laplacian matrices for each order of interactions (e.g., dyadic, triadic, etc.). However, this method can lose information about the hypergraph structure and is not universally applicable for studying dynamical processes on hypergraphs, which we demonstrate through the framework of cluster synchronization. Specifically, we show that the projected network does not always correspond to a unique hypergraph structure. This means the projection does not always properly predict the true dynamics unfolding on the hypergraph. Additionally, we show that the symmetry group consisting of permutations that preserve the hypergraph structure can be distinct from the symmetry group of its projected matrix. Thus, considering the full hypergraph is required for analyzing the most general types of dynamics on hypergraphs. We show that a formulation based on node clusters and the corresponding edge clusters induced by the node partitioning, enables the analysis of admissible patterns of cluster synchronization and their effective dynamics. Additionally, we show that the coupling matrix projections corresponding to each edge cluster synchronization pattern, and not just to each order of interactions, are necessary for understanding the structure of the Jacobian matrix and performing the linear stability calculations efficiently.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploring the Non-uniqueness of Node Co-occurrence Matrices of Hypergraphs

    cs.SI 2025-06 conditional novelty 6.0 of 10

    TwinSearch enumerates all twin hypergraphs of a node co-occurrence matrix and shows non-uniqueness is common and structurally diverse for small random hypergraphs.

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