Titlebook: Complex Networks and Their Applications VIII; Volume 2 Proceedings Hocine Cherifi,Sabrina Gaito,Luis Mateus Rocha Conference proceedings 20

形狀

Why We Need a Process-Driven Network Analysise applied. A network representation is suitable if indirect effects are of interest: if A has an impact on B and B has an impact on C, it is assumed that also A has an impact on C. This implies that some process is flowing through the network. For a meaningful network analysis, the network process,

小說(shuō)

生命層

BRIBE

Investigating Saturation in Collaboration and Cohesiveness of Wikipedia Using Motifs Analysisgrow exponentially. Further growth analysis of Wikipedia shows slowdown or saturation in its contents. In this paper, we investigate whether two essential characteristics of Wikipedia, collaboration and cohesiveness also encounter the phenomenon of slowdown or saturation with time. Collaboration in

Benzodiazepines

Crepitus

Understanding Dynamics of Truck Co-Driving Networksntial positive impact on the environment. In the so-called co-driving network, trucks are nodes while links indicate that two trucks frequently drive together. To understand the network’s dynamics, we use a . approach employing a machine learning classifier. The features of the classifier can be cat

無(wú)王時(shí)期,

吸引人的花招

A Network-Based Approach for Reducing Test Suites While Maintaining Code Coverage refactoring, etc. An ample test suite, while taking considerable resources and time to execute, could be as effective as a smaller one having the same code coverage. This paper proposes an approach to reduce the number of test cases needed while ensuring the same code coverage of a large test suite

stress-test

excrete

Comparing Spectra of Graph Shift Operator Matricesised Laplacian matrices. To enable a sensible comparison of their spectral (eigenvalue) properties, an affine transform is first applied to one of them, which preserves eigengaps. Bounds, which depend on the minimum and maximum degree of the network, are given on the resulting eigenvalue differences