scholarly journals Extracting the multiscale backbone of complex weighted networks

2009 ◽  
Vol 106 (16) ◽  
pp. 6483-6488 ◽  
Author(s):  
M. A. Serrano ◽  
M. Boguna ◽  
A. Vespignani
Keyword(s):  
Weighted Networks ◽  
Complex Weighted Networks

scholarly journals A comparative analysis of link removal strategies in real complex weighted networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
M. Bellingeri ◽  
D. Bevacqua ◽  
F. Scotognella ◽  
R. Alfieri ◽  
D. Cassi
Keyword(s):  
Comparative Analysis ◽  
Weighted Networks ◽  
Real Complex ◽  
Complex Weighted Networks

scholarly journals New Betweenness Centrality Node Attack Strategies for Real-World Complex Weighted Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Quang Nguyen ◽  
Ngoc-Kim-Khanh Nguyen ◽  
Davide Cassi ◽  
Michele Bellingeri
Keyword(s):  
Real World ◽  
Betweenness Centrality ◽  
Final Phase ◽  
Weighted Networks ◽  
Removal Process ◽  
Topological Measure ◽  
Connected Cluster ◽  
Small Clusters ◽  
Complex Weighted Networks ◽  
Attack Strategy

In this work, we introduce a new node attack strategy removing nodes with the highest conditional weighted betweenness centrality (CondWBet), which combines the weighted structure of the network and the node’s conditional betweenness. We compare its efficacy with well-known attack strategies from literature over five real-world complex weighted networks. We use the network weighted efficiency (WEFF) like a measure encompassing the weighted structure of the network, in addition to the commonly used binary-topological measure, i.e., the largest connected cluster (LCC). We find that if the measure is WEFF, the CondWBet strategy is the best to decrease WEFF in 3 out of 5 cases. Further, CondWBet is the most effective strategy to reduce WEFF at the beginning of the removal process, whereas the Strength that removes nodes with the highest sum of the link weights first shows the highest efficacy in the final phase of the removal process when the network is broken into many small clusters. These last outcomes would suggest that a better attacking in weighted networks strategy could be a combination of the CondWBet and Strength strategies.

Degree mixing and the enhancement of synchronization in complex weighted networks

2006 ◽  
Vol 74 (6) ◽  
Author(s):  
M. Chavez ◽  
D.-U. Hwang ◽  
J. Martinerie ◽  
S. Boccaletti
Keyword(s):  
Weighted Networks ◽  
Complex Weighted Networks

scholarly journals Consistency of heterogeneous synchronization patterns in complex weighted networks

2017 ◽  
Vol 27 (3) ◽  
pp. 031102 ◽  
Author(s):  
D. Malagarriga ◽  
A. E. P. Villa ◽  
J. Garcia-Ojalvo ◽  
A. J. Pons
Keyword(s):  
Weighted Networks ◽  
Complex Weighted Networks

scholarly journals An efficient algorithm for community detection in complex weighted networks

AIChE Journal ◽  
2021 ◽  
Author(s):  
Leila Samandari Masooleh ◽  
Jeffrey E. Arbogast ◽  
Warren D. Seider ◽  
Ulku Oktem ◽  
Masoud Soroush
Keyword(s):  
Community Detection ◽  
Efficient Algorithm ◽  
Weighted Networks ◽  
Complex Weighted Networks

scholarly journals An Efficient Algorithm for Community Detection in Complex Weighted Networks

2020 ◽  
Author(s):  
Leila Masooleh ◽  
Jeffrey Arbogast ◽  
Warren Seider ◽  
Ulku Oktem ◽  
Masoud Soroush
Keyword(s):  
Community Detection ◽  
Efficient Algorithm ◽  
Weighted Networks ◽  
Complex Weighted Networks

scholarly journals Comparison of ancient and modern Chinese based on complex weighted networks

PLoS ONE ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. e0187854
Author(s):  
Xinru Cui ◽  
Jinxu Qi ◽  
Hao Tan ◽  
Feng Chen
Keyword(s):  
Weighted Networks ◽  
Modern Chinese ◽  
Complex Weighted Networks
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