ArticleScientific reports2017
Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- The cingulum as a marker of individual differences in neurocognitive development.Scientific reports · 2019Trial
- GraphBNC: Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins.Advanced materials (Deerfield Beach, Fla.) · 2024Article
- Fractal Similarity of Pain Brain Networks.Advances in neurobiology · 2024Article
- GAHLS: an optimized graph analytics based high level synthesis framework.Scientific reports · 2023Article
- A unified approach of detecting phase transition in time-varying complex networks.Scientific reports · 2023Article
- Multifractality of Complex Networks Is Also Due to Geometry: A Geometric Sandbox Algorithm.Entropy (Basel, Switzerland) · 2023Article
- Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches.PLoS computational biology · 2023Article
- Network bypasses sustain complexity.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity.PloS one · 2023Article
- Community detection in networks by dynamical optimal transport formulation.Scientific reports · 2022Article
- Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations.The European journal of neuroscience · 2022Review
- Sexual dimorphism in the relationship between brain complexity, volume and general intelligence (g): a cross-cohort study.Scientific reports · 2022Article
- Editorial: Inference, Causality and Control in Networks of Dynamical Systems: Data Science and Modeling Perspectives to Network Physiology With Implications for Artificial Intelligence.Frontiers in physiology · 2022Article
- Novel Texture Feature Descriptors Based on Multi-Fractal Analysis and LBP for Classifying Breast Density in Mammograms.Journal of imaging · 2021Article
- Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature.Nature communications · 2021Article
- Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena.Scientific reports · 2021Article
- Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction.Entropy (Basel, Switzerland) · 2021Article
- Network extraction by routing optimization.Scientific reports · 2020Article
- Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data.Scientific reports · 2020Article
- Controlling the Multifractal Generating Measures of Complex Networks.Scientific reports · 2020Article
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2 authors.
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Abstract
Through an elegant geometrical interpretation, the multi-fractal analysis quantifies the spatial and temporal irregularities of the structural and dynamical formation of complex networks. Despite its effectiveness in unweighted networks, the multi-fractal geometry of weighted complex networks, the role of interaction intensity, the influence of the embedding metric spaces and the design of reliable estimation algorithms remain open challenges. To address these challenges, we present a set of reliable multi-fractal estimation algorithms for quantifying the structural complexity and heterogeneity of weighted complex networks. Our methodology uncovers that (i) the weights of complex networks and their underlying metric spaces play a key role in dictating the existence of multi-fractal scaling and (ii) the multi-fractal scaling can be localized in both space and scales. In addition, this multi-fractal characterization framework enables the construction of a scaling-based similarity metric and the identification of community structure of human brain connectome. The detected communities are accurately aligned with the biological brain connectivity patterns. This characterization framework has no constraint on the target network and can thus be leveraged as a basis for both structural and dynamic analysis of networks in a wide spectrum of applications.
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Registered trials
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 OpenQuestion graph.