Complex networks and their applications in information search

The science of complex networks is at the intersection of mathematics, computer science, physics, biology, economics and many other disciplines. In my report, I will pay the most attention to networks that describe the behavior of the Web (the Internet). It will focus on the construction of probabilistic models of such networks, their analysis and application of the results obtained for information retrieval tasks.
A.M. Raigorodskii, Models of Random Graphs and Their Applications to the Web-Graph Analysis, Communications in Computer and Information Science, Information Retrieval, P. Braslavski et al. eds., Springer, 573 (2016), 101 - 118.
Bogolubsky, L., Dvurechensky, P., Gasnikov, A., Gusev, G., Nesterov, Y., Raigorodskii, A.M., ... Zhukovskii, M. (2016). Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods. In Advances in Neural Information Processing Systems (pp. 4907-4915). Articles: E.A. Grechnikov, G.G. Gusev, L.A. Ostroumova, Yu.L. Pritykin, A.M. Raigorodskii, P. Serdyukov, D.V. Vinogradov, M.E. Zhukovskiy, Empirical Validation of the Buckley–Osthus Model for the Web Host Graph, the proceedings of The 21st ACM Conference on Information and Knowledge Management, 2012, 1577 - 1581.