Community Detection Algorithms in Large Networks

Student: Zarubina Margarita

Faculty: Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod)

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

Standard portfolio optimization methods do not use data structure. In this paper was described on approach, which was conveniently incorporated into the analysis investment returns, by graphs. The main idea is very clear to intuition understanding: the key pattern is to implement graph cuts paradigm. The implementing of clustering algorithms for creating optimal portfolio is shown in this work. It leads to devise robust and tractable asset allocation schemes, by virtue of a rigorous graph structure for identifying clusters of assets, based on graph cuts. The main benefit of this concept is reductions in the computing complexity by reduction in the number of vertex, which are involved in a optimization part of the algorithm. The paper provides some numerical simulations based on real-world price data of five stocks.

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