Reconstructing weighted networks from dynamics

2015 ◽  
Vol 91 (3) ◽  
Author(s):  
Emily S. C. Ching ◽  
Pik-Yin Lai ◽  
C. Y. Leung
Keyword(s):  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linqing Liu ◽  
Mengyun Shen ◽  
Chang Tan

AbstractFailing to consider the strong correlations between weights and topological properties in capacity-weighted networks renders test results on the scale-free property unreliable. According to the preferential attachment mechanism, existing high-degree nodes normally attract new nodes. However, in capacity-weighted networks, the weights of existing edges increase as the network grows. We propose an optimized simplification method and apply it to international trade networks. Our study covers more than 1200 product categories annually from 1995 to 2018. We find that, on average, 38%, 38% and 69% of product networks in export, import and total trade are scale-free. Furthermore, the scale-free characteristics differ depending on the technology. Counter to expectations, the exports of high-technology products are distributed worldwide rather than concentrated in a few developed countries. Our research extends the scale-free exploration of capacity-weighted networks and demonstrates that choosing appropriate filtering methods can clarify the properties of complex networks.


2017 ◽  
Vol 95 (4) ◽  
Author(s):  
Francesco Alderisio ◽  
Gianfranco Fiore ◽  
Mario di Bernardo

2020 ◽  
Vol 30 (12) ◽  
pp. 123146
Author(s):  
Daniel Monsivais-Velazquez ◽  
Kunal Bhattacharya ◽  
Rafael A. Barrio ◽  
Philip K. Maini ◽  
Kimmo K. Kaski

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Dai-Jun Wei ◽  
Qi Liu ◽  
Hai-Xin Zhang ◽  
Yong Hu ◽  
Yong Deng ◽  
...  

2019 ◽  
Vol 519 ◽  
pp. 303-312 ◽  
Author(s):  
Hong-liang Sun ◽  
Duan-bing Chen ◽  
Jia-lin He ◽  
Eugene Ch’ng
Keyword(s):  

2021 ◽  
Author(s):  
Yang Yu ◽  
Dezhou Kong

Abstract Background Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed to solve this issue. These algorithms usually consider a node’s direct neighbors and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection.Results To overcome this deficiency, this paper proposes a new protein complex identification method based on node-local topological properties and gene expression information on a new weighted PPI network, named NLPGE-WPN (joint node-local topological properties and gene expression information on weighted PPI network). First, based on the resource allocation of the PPI network and gene expression, a new weight metric is designed to describe the interaction between proteins. Second, our method constructs a series of dense complex cores based on density and network diameter constraints; the final complexes are recognized by expanding the second-order neighbor nodes of core complexes. Experimental results demonstrate that this algorithm has improved the performances of precision and f-measure, which is more valid in identifying protein complexes.Conclusions This identification method is simple and can accurately identify more complexes by integrating node-local properties and gene expression on PPI weighted networks.


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