This is the github repo for sharing the code for implementing the Graph Markov Network (GMN) proposed in [1]. The GMN is proposed to solve the traffic forecasting problems while the traffic data has ...

Abstract: In the field of computer science, the network reliability problem for evaluating the network failure probability has been extensively investigated. For a given undirected graph G, the ...

Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant ...

Graphs are the ultimate tools of storytelling for Data scientists and engineers, but there exists another type of graph called code graphs. These graphs are the visual representation of the code and ...

Abstract: Random anomalous behavior is a false or redundant behavior that randomly appears in the network structure, affecting the analysis result of the network. Current methods mainly capture the ...

Julia and Python complex system applications in ecology, epidemiology, sociology, economics & finance; network science models including Bianconi-Barabási, Barabási-Albert, Watts-Strogatz, Waxman Model ...

In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However ...

Our data science expert continues his exploration of neural network programming, explaining how regularization addresses the problem of model overfitting, caused by network overtraining. Neural ...