A Novel Architecture Design for Complex Network Measures of Brain Connectivity Aiding Diagnosis

2021 ◽  
pp. 281-302
Author(s):  
Chandrajit Pal ◽  
Amit Acharyya
Keyword(s):  
Complex Network ◽  
Brain Connectivity ◽  
Architecture Design ◽  
Network Measures

Complex network measures of brain connectivity: Uses and interpretations

NeuroImage ◽  
2010 ◽  
Vol 52 (3) ◽  
pp. 1059-1069 ◽  
Author(s):  
Mikail Rubinov ◽  
Olaf Sporns
Keyword(s):  
Complex Network ◽  
Brain Connectivity ◽  
Network Measures

scholarly journals Brain connectivity and novel network measures for Alzheimer's disease classification

2015 ◽  
Vol 36 ◽  
pp. S121-S131 ◽  
Author(s):  
Gautam Prasad ◽  
Shantanu H. Joshi ◽  
Talia M. Nir ◽  
Arthur W. Toga ◽  
Paul M. Thompson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Connectivity ◽  
Disease Classification ◽  
Network Measures

The Complex Network of Architectural Design

2013 ◽  
Vol 357-360 ◽  
pp. 349-353
Author(s):  
Xiao Ming Hu ◽  
Qiang Liu
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Architectural Design ◽  
Design Thinking ◽  
Architecture Design ◽  
Complex Network Theory ◽  
Scientific Analysis

We all know that the design thinking in architecture design is quite complex. However, the most existing studies on design thinking have been based upon reductionism. By introducing complex network theory, complexity perspective is used to re-interpret the architectural design in this paper. Moreover, the characteristics, rules and thinking characteristics of architectural creation are revealed by the characteristics of complex network. The paper advocates combining holism and reductionism to guide the architectural design, and pointed out that synesthesia exists among different factors of complex network. Based upon the scientific analysis on several famous cases, the author pointed out the great importance of synesthesia in architects' design thinking.

scholarly journals Character networks and book genre classification

2019 ◽  
Vol 30 (08) ◽  
pp. 1950058
Author(s):  
Adriano J. Holanda ◽  
Mariane Matias ◽  
Sueli M. S. P. Ferreira ◽  
Gisele M. L. Benevides ◽  
Osame Kinouchi
Keyword(s):  
Social Networks ◽  
Complex Network ◽  
Power Law ◽  
Degree Distribution ◽  
Previous Literature ◽  
Literary Genre ◽  
Social Character ◽  
Network Measures ◽  
Genre Classification ◽  
The Social

We compared the social character networks of biographical, legendary and fictional texts in search for marks of genre differentiation. We examined the degree distribution of character appearance and found a power-law-like distribution that does not depend on the literary genre. We also analyzed local and global complex network measures, in particular, correlation plots between the recently introduced Lobby index and degree, betweenness and closeness centralities. Assortativity plots, which previous literature claims to separate fictional from real social networks, were also studied. We found no relevant differences among genres for the books studied when applying these network measures and we provide an explanation why the previous assortativity result is not correct.

Complex Network Measures for Data Classification

2021 ◽  
Author(s):  
Murillo G. Carneiro ◽  
Barbara C. Gama ◽  
Otavio S. Ribeiro
Keyword(s):  
Complex Network ◽  
Data Classification ◽  
Network Measures

Complex Network Measures in Autism Spectrum Disorders

2018 ◽  
Vol 15 (2) ◽  
pp. 581-587 ◽  
Author(s):  
Joao Ricardo Sato ◽  
Maciel Calebe Vidal ◽  
Suzana de Siqueira Santos ◽  
Katlin Brauer Massirer ◽  
Andre Fujita
Keyword(s):  
Autism Spectrum Disorders ◽  
Complex Network ◽  
Autism Spectrum ◽  
Network Measures ◽  
Spectrum Disorders

Network Measures and Brain connectivity Analysis of EEG Signal of Alzheimer’s Patients Using SVM

2021 ◽  
Author(s):  
Ruofan Wang ◽  
Yiyang Yin ◽  
Ying Gui ◽  
Haodong Wang ◽  
Lianshuan Shi
Keyword(s):  
Brain Connectivity ◽  
Eeg Signal ◽  
Connectivity Analysis ◽  
Network Measures

Analysis of Complex Network Measures for Multi-label Classification

2021 ◽  
Vol 30 (04) ◽  
pp. 2150023
Author(s):  
Vinícius H. Resende ◽  
Murillo G. Carneiro
Keyword(s):  
Complex Network ◽  
Predictive Performance ◽  
Clustering Coefficient ◽  
Data Sets ◽  
Real World Data ◽  
Network Measures ◽  
Formation Pattern ◽  
Network Properties ◽  
High Level ◽  
Physical Features

Most multi-label learning (MLL) techniques perform classification by analyzing only the physical features of the data, which means they are unable to consider high-level features, such as structural and topological ones. Consequently, they have trouble to detect the semantic meaning of the data (e.g., formation pattern). To handle this problem, a high-level framework has been recently proposed to the MLL task, in which the high-level features are extracted using the analysis of complex network measures. In this paper, we extend that work by evaluating different combinations of four complex networks measures, namely clustering coefficient, assortativity, average degree and average path length. Experiments conducted over seven real-world data sets showed that the low-level techniques often can have their predictive performance improved after being combined with high-level ones, and also demonstrated that there is no a unique measure that provides the best results, i.e., different problems may ask for different network properties in order to have their high-level patterns efficiently detected.

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