Automatic generation of adaptive network models based on similarity to the desired complex network

Attar, Niousha; Aliakbary, Sadegh; Nezhad, Zahra Hosseini

doi:10.1016/j.physa.2019.123353

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…Consequently, mechanisms like preferential attachment [36] and homophily [37] have been identified as driving forces for interactions in a network. These mechanism coupled with the observation that stochastic local interactions give rise to emergent global structure [38] has inspired researchers to combine multiple mechanisms for link formation in networks [39,40,31,41,42,43].…”

Section: Introductionmentioning

confidence: 99%

Investigating cognitive ability using action-based models of structural brain networks

Arora¹,

Amico²,

Goñi³

et al. 2022

Preprint

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Recent developments in network neuroscience have highlighted the importance of developing techniques for analyzing and modeling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use wiring rules to synthesize networks closely resembling the topology of a given connectome. Successful models can highlight the principles by which a network is organized (identify structural features that arise from wiring rules versus those that emerge) and potentially uncover the mechanisms by which it grows and develops. Previous research has shown that such models can validate the effectiveness of spatial embedding and other (non-spatial) wiring rules in shaping the network topology of the human connectome. In this research, we propose variants of the action-based model that combine a variety of generative factors capable of explaining the topology of the human connectome. We test the descriptive validity of our models by evaluating their ability to explain between-subject variability. Our analysis provides evidence that geometric constraints are vital for connectivity between brain regions, and an action-based model relying on both topological and geometric properties can account for between-subject variability in structural network properties. Further, we test correlations between parameters of subject-optimized models and various measures of cognitive ability and find that higher cognitive ability is associated with an individual's tendency to form long-range or non-local connections.

show abstract

Section: Introductionmentioning

confidence: 99%

Investigating cognitive ability using action-based models of structural brain networks

Arora¹,

Amico²,

Goñi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Random or preferential? Evolutionary mechanism of user behavior in co-creation community

Zhang

Cao

et al. 2022

Comput Math Organ Theory

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Investigating cognitive ability using action-based models of structural brain networks

Arora

Amico

Goñi

et al. 2022

Journal of Complex Networks

View full text Add to dashboard Cite

Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use wiring rules to synthesize networks closely resembling the topology of a given connectome. Successful models can highlight the principles by which a network is organized (identify structural features that arise from wiring rules versus those that emerge) and potentially uncover the mechanisms by which it grows and develops. Previous research has shown that such models can validate the effectiveness of spatial embedding and other (non-spatial) wiring rules in shaping the network topology of the human connectome. In this research, we propose variants of the action-based model that combine a variety of generative factors capable of explaining the topology of the human connectome. We test the descriptive validity of our models by evaluating their ability to explain between-subject variability. Our analysis provides evidence that geometric constraints are vital for connectivity between brain regions, and an action-based model relying on both topological and geometric properties can account for between-subject variability in structural network properties. Further, we test correlations between parameters of subject-optimized models and various measures of cognitive ability and find that higher cognitive ability is associated with an individual’s tendency to form long-range or non-local connections.

show abstract

Automatic generation of adaptive network models based on similarity to the desired complex network

Cited by 3 publications

References 44 publications

Investigating cognitive ability using action-based models of structural brain networks

Investigating cognitive ability using action-based models of structural brain networks

Random or preferential? Evolutionary mechanism of user behavior in co-creation community

Investigating cognitive ability using action-based models of structural brain networks

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