Multi-scale Laplacian community detection in heterogeneous networks

Physical Review Research, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 17, 2025

Heterogeneous and complex networks represent intertwined interactions between real-world elements or agents. Determining the multiscale mesoscopic organization of clusters structures is still a fundamental open problem network theory. By taking advantage recent Laplacian renormalization group (LRG), we scrutinize information diffusion pathways throughout to shed further light on this issue. Based internode communicability, our definition provides clear-cut framework for resolving mesh in networks, disentangling their intrinsic arboreal architecture. As it does not consider any topological null-model assumption, LRG naturally permits introduction scale-dependent optimal partitions. Moreover, demonstrate existence particular class nodes, called that switch regions which they belong at different scales, likely playing pivotal role cross-regional communication and, therefore, managing macroscopic effects whole network. Published by American Physical Society 2025

Language: Английский

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Networks with Many Structural Scales: A Renormalization Group Perspective

Physical Review Letters, Journal Year: 2025, Volume and Issue: 134(5)

Published: Feb. 5, 2025

Scale invariance profoundly influences the dynamics and structure of complex systems, spanning from critical phenomena to network architecture. Here, we propose a precise definition scale-invariant networks by leveraging concept constant entropy-loss rate across scales in renormalization-group coarse-graining setting. This framework enables us differentiate between scale-free networks, revealing distinct characteristics within each class. Furthermore, offer comprehensive inventory genuinely both natural artificially constructed, demonstrating, e.g., that human connectome exhibits notable features scale invariance. Our findings open new avenues for exploring structural properties crucial biological sociotechnological systems.

Language: Английский

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Diversity of information pathways drives sparsity in real-world networks

Nature Physics, Journal Year: 2024, Volume and Issue: 20(3), P. 512 - 519

Published: Jan. 17, 2024

Language: Английский

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Information Propagation in Multilayer Systems with Higher-Order Interactions across Timescales

Physical Review X, Journal Year: 2024, Volume and Issue: 14(2)

Published: April 8, 2024

Complex systems are characterized by multiple spatial and temporal scales. A natural framework to capture their multiscale nature is that of multilayer networks, where different layers represent distinct physical processes often regulate each other indirectly. We model these regulatory mechanisms through triadic higher-order interactions between nodes edges. In this work, we focus on how the timescales associated with layer impact reciprocal effective couplings. First, rigorously derive a decomposition joint probability distribution any dynamical process acting such networks. By inspecting probabilistic structure, unravel general principles governing information propagates across timescales, elucidating interplay mutual causality in systems. particular, show feedback interactions, i.e., those representing from slow fast variables, generate layers. On contrary, direct layers, can propagate only under certain conditions depend solely structure underlying introduce matrix for observables emergent functional apply our results study archetypal examples biological signaling networks environmental dependencies stochastic processes. Our generalizes dynamics paving way deeper understanding real-world shapes content complexity. Published American Physical Society 2024

Language: Английский

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Coarse-graining network flow through statistical physics and machine learning

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 13, 2025

Information dynamics plays a crucial role in complex systems, from cells to societies. Recent advances statistical physics have made it possible capture key network properties, such as flow diversity and signal speed, using entropy free energy. However, large system sizes pose computational challenges. We use graph neural networks identify suitable groups of components for coarse-graining achieve low complexity, practical application. Our approach preserves information even under significant compression, shown through theoretical analysis experiments on synthetic empirical networks. find that the model merges nodes with similar structural suggesting they perform redundant roles transmission. This method enables low-complexity compression extremely networks, offering multiscale perspective biological, social, technological better than existing methods mostly focused structure. are systems. The authors apply group reducing complexity. Their being effective

Language: Английский

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Higher-order Laplacian renormalization

Nature Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Language: Английский

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Scale-dependent fracture networks

Journal of Structural Geology, Journal Year: 2022, Volume and Issue: 165, P. 104748 - 104748

Published: Oct. 30, 2022

Using examples of regional opening-mode fractures in sandstones from the Cambrian Flathead Formation, Wyoming, we show that quartz deposits preferentially fill up to ca. 0.05 mm wide and transition being mostly sealed open over a narrow size range opening displacements 0.1 mm. In our example, although isolated (I-node) dominated networks have some trace connectivity, effective connectivity for fluid flow is likely greatly reduced by cementation. Trace at microscopic outcrop scale similar, but most porosity found outcrop-scale fractures. Near faults, increases as initially porous shear wing cracks form, increasing fracture intersections (Y-nodes). However, pore space lost due development microbreccia. Macro-scale increases, diminishes thus potential markedly lower. Connectivity descriptions should include accurate measures widths lengths use nodes reflect diagenesis. We propose new rule-based node measure diagenesis sensitive connections within context current field practices. Under diagenetic conditions between 50°C–250°C differential infill makes network porosity, permeability strength, dependent.

Language: Английский

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Does the brain behave like a (complex) network? I. Dynamics

Physics of Life Reviews, Journal Year: 2023, Volume and Issue: 48, P. 47 - 98

Published: Dec. 12, 2023

Language: Английский

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Geometric renormalization of weighted networks

Communications Physics, Journal Year: 2024, Volume and Issue: 7(1)

Published: March 15, 2024

Abstract The geometric renormalization technique for complex networks has successfully revealed the multiscale self-similarity of real network topologies and can be applied to generate replicas at different length scales. Here, we extend framework weighted networks, where intensities interactions play a crucial role in their structural organization function. Our findings demonstrate that exhibits under protocol selects connections with maximum weight across increasingly longer We present theory elucidates this symmetry, sustains selection as meaningful procedure. Based on our results, scaled-down straightforwardly derived, facilitating investigation various size-dependent phenomena downstream applications.

Language: Английский

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Physical networks as network-of-networks

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 7, 2024

Language: Английский

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