Networks' modulation: How different structural network properties affect the global synchronization of coupled Kuramoto oscillators

• URL: http://arxiv.org/abs/2303.03099v1
• Date: Fri, 24 Feb 2023 13:21:58 GMT
• Title: Networks' modulation: How different structural network properties affect the global synchronization of coupled Kuramoto oscillators
• Authors: Juliette Courson, Thanos Manos and Mathias Quoy
• Abstract summary: synchronization occurs when different oscillating objects tune their rhythm when they interact with each other. We study the impact of different network architectures, such as Fully-Connected, Random, Regular ring lattice graph, Small-World and Scale-Free. Our main finding is, that in Scale-Free and Random networks a sophisticated choice of nodes based on their eigenvector centrality and average shortest path length exhibits a systematic trend in achieving higher degree of synchrony.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a large variety of systems (biological, physical, social etc.), synchronization occurs when different oscillating objects tune their rhythm when they interact with each other. The different underlying network defining the connectivity properties among these objects drives the global dynamics in a complex fashion and affects the global degree of synchrony of the system. Here we study the impact of such types of different network architectures, such as Fully-Connected, Random, Regular ring lattice graph, Small-World and Scale-Free in the global dynamical activity of a system of coupled Kuramoto phase oscillators. We fix the external stimulation parameters and we measure the global degree of synchrony when different fractions of nodes receive stimulus. These nodes are chosen either randomly or based on their respective strong/weak connectivity properties (centrality, shortest path length and clustering coefficient). Our main finding is, that in Scale-Free and Random networks a sophisticated choice of nodes based on their eigenvector centrality and average shortest path length exhibits a systematic trend in achieving higher degree of synchrony. However, this trend does not occur when using the clustering coefficient as a criterion. For the other types of graphs considered, the choice of the stimulated nodes (randomly vs selectively using the aforementioned criteria) does not seem to have a noticeable effect.

Related papers

• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs [55.51300642911766]
  Federated Graph Learning (FGL) empowers clients to collaboratively train Graph neural networks (GNNs) in a distributed manner. FGL methods usually require that the graph data owned by all clients is homophilic to ensure similar neighbor distribution patterns of nodes. We propose FedHERO, an FGL framework designed to harness and share insights from heterophilic graphs effectively.
  arXiv  Detail & Related papers  (2025-04-29T22:23:35Z)
• Allostatic Control of Persistent States in Spiking Neural Networks for perception and computation [79.16635054977068]
  We introduce a novel model for updating perceptual beliefs about the environment by extending the concept of Allostasis to the control of internal representations. In this paper, we focus on an application in numerical cognition, where a bump of activity in an attractor network is used as a spatial numerical representation.
  arXiv  Detail & Related papers  (2025-03-20T12:28:08Z)
• Harnessing omnipresent oscillator networks as computational resource [0.0]
  We present a universal framework for harnessing oscillator networks as computational resource. We force the Kuramoto model by a nonlinear target-system, then after substituting the target-system with a trained feedback-loop it emulates the target-system.
  arXiv  Detail & Related papers  (2025-02-07T10:42:01Z)
• Discovering Message Passing Hierarchies for Mesh-Based Physics Simulation [61.89682310797067]
  We introduce DHMP, which learns Dynamic Hierarchies for Message Passing networks through a differentiable node selection method. Our experiments demonstrate the effectiveness of DHMP, achieving 22.7% improvement on average compared to recent fixed-hierarchy message passing networks.
  arXiv  Detail & Related papers  (2024-10-03T15:18:00Z)
• A Dirichlet stochastic block model for composition-weighted networks [0.0]
  We propose a block model for composition-weighted networks based on direct modelling of compositional weight vectors. Inference is implemented via an extension of the classification expectation-maximisation algorithm. The model is validated using simulation studies, and showcased on network data from the Erasmus exchange program and a bike sharing network for the city of London.
  arXiv  Detail & Related papers  (2024-08-01T15:41:07Z)
• Federated Graph Semantic and Structural Learning [54.97668931176513]
  This paper reveals that local client distortion is brought by both node-level semantics and graph-level structure. We postulate that a well-structural graph neural network possesses similarity for neighbors due to the inherent adjacency relationships. We transform the adjacency relationships into the similarity distribution and leverage the global model to distill the relation knowledge into the local model.
  arXiv  Detail & Related papers  (2024-06-27T07:08:28Z)
• Discovering group dynamics in synchronous time series via hierarchical recurrent switching-state models [3.5902825399592193]
  We seek to model a collection of time series arising from multiple entities interacting over the same time period. Recent work focused on modeling individual time series is inadequate for our intended applications, where collective system-level behavior influences the trajectories of individual entities. We employ a latent system-level discrete state chain that drives latent entity-level chains which in turn govern the dynamics of each observed time series.
  arXiv  Detail & Related papers  (2024-01-26T16:06:01Z)
• Inferring the Graph of Networked Dynamical Systems under Partial Observability and Spatially Colored Noise [2.362288417229025]
  In a Networked Dynamical System (NDS), each node is a system whose dynamics are coupled with the dynamics of neighboring nodes. The underlying network is unknown in many applications and should be inferred from observed data.
  arXiv  Detail & Related papers  (2023-12-18T16:19:07Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Localization and delocalization in networks with varied connectivity [0.0]
  We study the phenomenon of localization and delocalization in a circuit-QED network with connectivity varying from finite-range to all-to-all coupling. The interacting cases (Jaynes-Cummings, Bose-Hubbard networks) are investigated both via exact quantum dynamics and semi-classical approach.
  arXiv  Detail & Related papers  (2022-02-24T18:01:38Z)
• KuraNet: Systems of Coupled Oscillators that Learn to Synchronize [8.53236289790987]
  "KuraNet" is a deep-learning system of coupled oscillators that can learn to synchronize across a distribution of disordered network conditions. We show how KuraNet can be used to empirically explore the conditions of global synchrony in analytically impenetrable models with disordered natural frequencies, external field strengths, and interaction delays. In all cases, we show how KuraNet can generalize to both new data and new network scales, making it easy to work with small systems and form hypotheses about the thermodynamic limit.
  arXiv  Detail & Related papers  (2021-05-06T17:26:33Z)
• Graph Gamma Process Generalized Linear Dynamical Systems [60.467040479276704]
  We introduce graph gamma process (GGP) linear dynamical systems to model real multivariate time series. For temporal pattern discovery, the latent representation under the model is used to decompose the time series into a parsimonious set of multivariate sub-sequences. We use the generated random graph, whose number of nonzero-degree nodes is finite, to define both the sparsity pattern and dimension of the latent state transition matrix.
  arXiv  Detail & Related papers  (2020-07-25T04:16:34Z)
• Unsupervised Differentiable Multi-aspect Network Embedding [52.981277420394846]
  We propose a novel end-to-end framework for multi-aspect network embedding, called asp2vec. Our proposed framework can be readily extended to heterogeneous networks.
  arXiv  Detail & Related papers  (2020-06-07T19:26:20Z)
• ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings [54.33327082243022]
  ClusterVO is a stereo Visual Odometry which simultaneously clusters and estimates the motion of both ego and surrounding rigid clusters/objects. Unlike previous solutions relying on batch input or imposing priors on scene structure or dynamic object models, ClusterVO is online, general and thus can be used in various scenarios including indoor scene understanding and autonomous driving.
  arXiv  Detail & Related papers  (2020-03-29T09:06:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.