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Titlebook: Network Algorithms, Data Mining, and Applications; NET, Moscow, Russia, Ilya Bychkov,Valery A. Kalyagin,Oleg Prokopyev Conference proceedin

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樓主: Hoover
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發(fā)表于 2025-3-28 17:56:29 | 只看該作者
42#
發(fā)表于 2025-3-28 20:44:10 | 只看該作者
Conference proceedings 2020018. The conference brought together scientists, engineers, and researchers from academia, industry, and government.?.Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, comput
43#
發(fā)表于 2025-3-29 02:23:32 | 只看該作者
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發(fā)表于 2025-3-29 06:39:56 | 只看該作者
Computational Complexity of SRIC and LRIC Indices consideration. Our main focus is on the performance of SRIC and LRIC indices. We propose several modes on how to decrease the computational complexity of these indices. The runtime comparison of the sequential and parallel computation of the proposed models is also given.
45#
發(fā)表于 2025-3-29 07:43:20 | 只看該作者
46#
發(fā)表于 2025-3-29 15:15:36 | 只看該作者
Social Mechanisms of the Subject Area Formation. The Case of “Digital Economy”twork modeling in the Text Analyst) and the analysis of semantic networks. The results, approaches, and methods of this study could be used during the investigation of the other large thematic fields related to new ideological currents, being developed as an element of social design and management.
47#
發(fā)表于 2025-3-29 18:50:08 | 只看該作者
Methodology for Measuring Polarization of Political Discourse: Case of Comparing Oppositional and Pas to understand social models and attitudes embedded in the discourse and the subsequent behavior of representatives of these groups. On the other hand, it is possible to extend a set of keywords for text search of a certain political orientation, based on the obtained results.
48#
發(fā)表于 2025-3-29 21:29:40 | 只看該作者
49#
發(fā)表于 2025-3-30 01:06:36 | 只看該作者
50#
發(fā)表于 2025-3-30 06:49:19 | 只看該作者
A Survey on Variable Neighborhood Search Methods for Supply Network Inventoryothers. In this chapter, we present recent successful applications of variable neighborhood search (VNS) for the efficient solution of such problems, review the state-of-the-art solution methods, and also discuss some future research directions.
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