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- W4313201208 abstract "Finding an influential set of nodes in the interconnected network, whose activities would maximally influence the development of overall network, is a fundamental topic in finance, biology, social science, epidemic control and other scenarios with respect to network science. In recent years, some works are proposed to find key nodes via network connectivity measures, these studies assume a static environment, and besides, key nodes are calculated through pairwise connectivity, the number of connected components and other measures from the perspective of graph theory. However, these studies leave out of consideration on the dynamic evolving environment and causal relationships in the network. In this paper, we introduce a key node learning algorithm in an evolving financial network. The intuition of the algorithm is to model the causality of financial entities over the time, and the metrics for cascading networks are proposed to grade the entities in the network. In the experiment, the worldwide financial activities before and during the period of coronavirus are analyzed. The proposed metrics can effectively capture the influential entities and quantitatively describe these influences on the whole financial system." @default.
- W4313201208 created "2023-01-06" @default.
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- W4313201208 date "2022-01-01" @default.
- W4313201208 modified "2023-09-25" @default.
- W4313201208 title "A dynamic key node learning algorithm in evolving financial network: case study during the period of coronavirus" @default.
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- W4313201208 doi "https://doi.org/10.1016/j.procs.2022.11.347" @default.
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