Finding key players in complex networks
WebSci-Hub Finding key players in complex networks through deep reinforcement learning. Nature Machine Intelligence, 2 (6), 317–324 10.1038/s42256-020-0177-2 sci hub to … WebAs we focus on both the efficiency and accuracy of the algorithm, three centralities with low computational complexity are introduced: the sum of neighbors’ degree, the number of communities a node is connected with, and the k-core value.
Finding key players in complex networks
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WebMay 25, 2024 · Finding key players in complex networks through deep reinforcement learning Changjun Fan, Li Zeng, Yizhou Sun & Yang-Yu Liu Nature Machine Intelligence 2 , 317–324 ( 2024) Cite this article... We would like to show you a description here but the site won’t allow us.
WebMar 4, 2024 · Identification of influential nodes in complex networks is an area of exciting growth since it can help us to deal with various problems. Furthermore, identifying … Webidentify key players in complex networks June 26 2024, by Ingrid Fadelli Finding key players in a network. (a) The 9/11 terrorist network, which contains 62 nodes and 159 edges. Nodes represent terrorists involved in the 9/11 attack, and edges represent their social communications. Node size is proportional to its degree.
Web本期给大家介绍2024年的一篇复杂网络论文：通过深度强化学习寻找复杂网络中的关键节点(Finding key players in complex networks throughdeep reinforcement learning)[1]，发 … WebOct 26, 2024 · According to the results, our proposed method outperforms classical methods in identifying influential nodes and also indicates the potential for analyzing the influence evolution of networks, which shows a positive and effective impact on locating influencers and predicting potential key players in the future.
WebMay 21, 2024 · Finding key players in complex networks through deep reinforcement learning Article Full-text available Jun 2024 Changjun Fan Zeng li Yizhou Sun Yang-Yu Liu View Show abstract Revisiting the...
WebFinding an optimal set of nodes, called key players, whose activation (or removal) would maximally enhance (or degrade) certain network functionality, is a fundamental class … glass blender just the glassWebMay 25, 2024 · A deep reinforcement learning framework that can be trained on small networks to understand the organizing principles of complex networked systems, which … glass blenders and processorsWebDec 1, 2024 · In this section, we first present the sequential-path trees, a new presentation structure of temporal networks in detail. Then, we introduce how to extract the three temporal features, i.e., propagation time, hop count, and reachable paths. fynd office mumbaiWebAug 1, 2024 · , A machine learning based framework for identifying influential nodes in complex networks, IEEE Access 8 (2024) 65462 – 65471. Google Scholar [17] Fan C.J., Zeng L., Sun Y.Z., Liu Y.Y., Finding key players in complex networks through deep reinforcement learning, Nat. Mach. Intell. 2 (6) (2024) 317 – 324. Google Scholar fynd office bangaloreWebJan 13, 2024 · Influence maximization (IM) in complex networks tries to activate a small subset of seed nodes that could maximize the propagation of influence. The studies on IM have attracted much attention due to their wide applications such as item recommendation, viral marketing, information propagation and disease immunization. Existing works … fynd onlineWebIdentifying key players in coupled individual systems is a fundamental problem in network theory. We investigate synchronizable network-coupled dynamical systems such as high … glass blind spot mirrors for carsWebIn 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. ... Yang-Yu Liu, Finding key players in … glass blenders for smoothies