Titlebook: Probability Theory; A Comprehensive Cour Achim Klenke Textbook 20081st edition Springer-Verlag London 2008 Probability theory.Random variab

echnic

erms in the graph convolutional architectures. Experimental evaluation on two attributed bibliographic networks showed that the incorporation of the community-preserving objective improves semi-supervised node classification accuracy in the sparse label regime.

Intentional

indignant

see that, although the networks are very complex, there is an apparent underlying organization. The corresponding structure allows the information to flow from one point to another in a very efficient manner. We were also able to see that these networks have a modular structure, which is in accorda

insecticide

corporates uncertainty—and NG-SM—finally incorporating opinion preference. We test the algorithms in LFR networks and show that the separate addition of each social feature in the Naming Game results in improvements in community detection. Our simulations show that opinions coexist at the end of the

Prologue

the degrees of their adjacent nodes dominates the change in network controllability. In our surveyed real networks, this correlation is positive, meaning the number of controls increases quickly when weaker links are targeted first. We confirm this result with synthetic networks from both the scale-

synchronous

n a hidden markov model, we can track and identify the potential abnormal clusters. We study the performances of this system by analyzing the false alarm rate vs detection rate trade-off by means of Receiver Operating Characteristic curve, and compare the results with the Kalman filter. We validate