Adaptive multi-resolution Modularity for detecting communities in networks

Chen, Shi; Wang, Zhizhong; Bao, Meihua; Tang, Liang; Zhou, Ji; Ju, Xu; Li, Jianming; Yi, Chen-He

doi:10.1016/j.physa.2017.09.023

Cited by 19 publications

(7 citation statements)

References 57 publications

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“…Starting from the hypothesis that the individual intrinsic brain signatures may be associated with the implicit sense of agency depending on the structural resolution of the modular architecture, and in line with recent multi-resolution approaches (Jeub et al, 2018;Chen et al, 2018), the modularity Q was analyzed using all the γ values in the interval [0.3-5.0]. Three different analyses were implemented to test these associations.…”

Section: Brain-behavior Relationship Analysismentioning

confidence: 99%

The Prospective Sense of Agency is Rooted in Local and Global Properties of Intrinsic Functional Brain Networks

Plinio

Perrucci

Ebisch

2020

Journal of Cognitive Neuroscience

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The sense of agency (SoA) refers to a constitutional aspect of the self describing the extent to which individuals feel in control over their actions and consequences thereof. Although the SoA has been associated with mental health and well-being, it is still unknown how interindividual variability in the SoA is embedded in the intrinsic brain organization. We hypothesized that the prospective component of an implicit SoA is associated with brain networks related to SoA and sensorimotor predictions on multiple spatial scales. We replicated previous findings by showing a significant prospective SoA as indicated by intentional binding effects. Then, using task-free fMRI and graph analysis, we analyzed associations between intentional binding effects and the intrinsic brain organization at regional, modular, and whole-brain scales. The results showed that intermodular connections of a frontoparietal module including the premotor cortex, supramarginal gyrus, and dorsal precuneus are associated with individual differences in prospective intentional binding. Notably, prospective intentional binding effects were also related to global brain modularity within a specific structural resolution range. These findings suggest that an implicit SoA generated through sensorimotor predictions relies on the intrinsic organization of the brain connectome on both local and global scales.

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Section: Brain-behavior Relationship Analysismentioning

confidence: 99%

The Prospective Sense of Agency is Rooted in Local and Global Properties of Intrinsic Functional Brain Networks

Plinio

Perrucci

Ebisch

2020

Journal of Cognitive Neuroscience

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“…Our analysis can be repurposed for different MB cohorts, available at data sharing platforms such as R2 ( http://r2.amc.nl ) and Cavatica ( www.cavatica.org ), among others. As for modularity, it is one of the most well-known quality functions for community detection ( Chen et al., 2018 ). Moreover, the Louvain algorithm has been adapted for multilayer networks ( Didier et al.…”

Section: Discussionmentioning

confidence: 99%

The multilayer community structure of medulloblastoma

et al. 2021

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Summary Multilayer networks allow interpreting the molecular basis of diseases, which is particularly challenging in rare diseases where the number of cases is small compared with the size of the associated multi-omics datasets. In this work, we develop a dimensionality reduction methodology to identify the minimal set of genes that characterize disease subgroups based on their persistent association in multilayer network communities. We use this approach to the study of medulloblastoma, a childhood brain tumor, using proteogenomic data. Our approach is able to recapitulate known medulloblastoma subgroups (accuracy >94%) and provide a clear characterization of gene associations, with the downstream implications for diagnosis and therapeutic interventions. We verified the general applicability of our method on an independent medulloblastoma dataset (accuracy >98%). This approach opens the door to a new generation of multilayer network-based methods able to overcome the specific dimensionality limitations of rare disease datasets.

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“…Although these methods have good reference value, this division has serious resolution limitation problem [15] , so many scholars target improved modularity for community detection, such as Mairisha et al [16] proposed an improved MC modularity to measure the modularity of a particular community by the grinding coefficient to get a more accurate community division. Chen et al [17] proposed an adaptive modularity calculation method that can combine the advantages of different modularity, and revealed the effectiveness and superiority of multi-resolution modularity in community detection by applying modularity to various synthetic networks and real-world networks. Qiao et al [18] proposed an overlapping community discovery algorithm in large data of complex networks, which is based on modularity for clustering, designed a new way of updating modularity, and then indexed the modularity increments by balanced binary trees to get a better community delineation.…”

Section: Introductionmentioning

confidence: 99%

An Improved Algorithm for Label Propagation Based on Rough Core

2022

AJCIS

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Complex networks are a hot topic in social science research today. Analyzing the structure of the relationships embedded in them through community discovery techniques is an important tool for the study of complex networks. To address the randomness problem of label propagation in label propagation algorithms, this paper proposes an improved algorithm for label propagation based on rough kernels. The algorithm first performs the extraction of rough kernels and assigns unique labels to each rough kernel to achieve fast label preprocessing; then intervenes in the label propagation process through the ranking of node influence to reduce its randomness and make the division results more stable; finally, the community division is performed by the distribution of network labels. Experiments are conducted in real networks, and the results show that the combined performance of the method in this paper is better than the traditional labeling algorithm in terms of modularity and NMI metrics, and the proposed improved algorithm leads to improved community division results.

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Adaptive multi-resolution Modularity for detecting communities in networks

Cited by 19 publications

References 57 publications

The Prospective Sense of Agency is Rooted in Local and Global Properties of Intrinsic Functional Brain Networks

The Prospective Sense of Agency is Rooted in Local and Global Properties of Intrinsic Functional Brain Networks

The multilayer community structure of medulloblastoma

An Improved Algorithm for Label Propagation Based on Rough Core

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