Titlebook: Complex Networks and Their Applications XI; Proceedings of The E Hocine Cherifi,Rosario Nunzio Mantegna,Salvatore M Conference proceedings

cerebral-cortex

书目名称Complex Networks and Their Applications XI影响因子(影响力)




书目名称Complex Networks and Their Applications XI影响因子(影响力)学科排名




书目名称Complex Networks and Their Applications XI网络公开度




书目名称Complex Networks and Their Applications XI网络公开度学科排名




书目名称Complex Networks and Their Applications XI被引频次




书目名称Complex Networks and Their Applications XI被引频次学科排名




书目名称Complex Networks and Their Applications XI年度引用




书目名称Complex Networks and Their Applications XI年度引用学科排名




书目名称Complex Networks and Their Applications XI读者反馈




书目名称Complex Networks and Their Applications XI读者反馈学科排名

Anthology

Factorable

A More Powerful Heuristic for Balancing an Unbalanced Graphose ‘+’/‘−’ labels can be flipped to balance .. Our algorithm finds a minimal flipping edge-set, starting with a given spanning tree . of ., by considering both the edges not in . and those in . because flipping a tree-edge can sometimes balance multiple fundamental unbalanced cycles at the same tim

Detonate

Grievance

A Stochastic Approach for Extracting Community-Based Backbonesd to build a compact version of the networks allowing their analysis is a matter of great importance. One of the main solutions to reduce the size of the network while maintaining its characteristics is backbone extraction techniques. Two types of methods are distinguished in the literature: similar

ureter

Correcting Output Degree Sequences in Chung-Lu Random Graph Generationng to their desired degrees to form a specific degree distribution in expectation. Despite its popularity, the standard Chung-Lu graph generation algorithms are susceptible to significant degree sequence errors when generating simple graphs. In this manuscript, we suggest multiple methods for improv

ureter

圆锥体

invade

Mechanics

A Biased Random Walk Scale-Free Network Growth Model with Tunable Clusteringsi-Albert model (BA) combines growth and preferential attachment to model the emergence of the power law. One builds a network by adding new nodes that preferentially link to high-degree nodes in the network. One can also exploit random walks. In this case, the network growth is determined by choosi