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Adjacency matrix nodebox10/26/2022 In LFIC, the uncertainty of information contained in nodes' boxes is measured by the improved Shannon entropy. LFIC uses the amount of information contained in the node's box as a measure of its importance. LFIC puts forward the concept that the inner structure of a node's box contains information about the node's importance. In this paper, a novel centrality measure based on Local Fuzzy Information Centrality (LFIC) is proposed. Recently, many methods have been proposed, but they suffer from certain limitations. The issue of mining influential nodes in complex networks is a topic of immense interest. The HIC centrality has low computational complexity, allowing it to be applied to sparse large-scale networks. Experimental results show that the proposed method is superior to seven contrast methods in terms of accuracy, effectiveness and distinguishing ability, including five centrality measures and two popular potential edge weight methods. #ADJACENCY MATRIX NODEBOX SERIES#To evaluate the performance of HIC, we adopt the susceptible-infected-recovered (SIR) model and the susceptible-infected (SI) model for simulation and conduct a series of comparative experiments on nine real-world networks and three artificial networks. The key idea of HIC is to incorporate neighborhood, position, and topological structure features to identify influential nodes. A novel potential edge weight method, called the HIC centrality (HIC), is proposed to identify influential nodes in complex networks. In this paper, we introduce a novel potential edge weight to distinguish the influence of each edge by utilizing the H-index, k-shell iteration factor and clustering coefficient of its connection nodes. Edges in networks are vital communication channels in the information spreading process, many existing methods for identifying influential nodes are established via employing characteristics of nodes, but most of them consider the edges equally in unweighted networks. The identification of influential nodes is one of the most urgent and challenging research issues in complex networks, which is crucial to the robustness and stability of networks. When paired with combinatorial synthesis, the correlation enhances throughput by up to 100 times compared to today’s state-of-the-art combinatorial methods and will facilitate the discovery of bulk metallic glasses. This correlation indicates that a large dispersion of structural units comprising the amorphous structure is the universal indicator for high metallic glass formation. A strong correlation between high glass forming ability and a large Δq was found. Specifically, we fabricated roughly 5,700 alloys from 12 alloy systems and characterized the full-width at half-maximum, Δq, of the first diffraction peak in the X-ray diffraction pattern. Here, we uncover that the glass forming ability of an alloy is represented in its amorphous structure far away from equilibrium, which can be exposed by conventional X-ray diffraction. Finally, we evaluate the model with several use cases based on real networks, two of them are proposed and created in this paper, giving insight into some interesting findings about the networks’ features.ĭespite the importance of glass forming ability as a major alloy characteristic, it is poorly understood and its quantification has been experimentally laborious and computationally challenging. This model, based on four indices, describes the behaviour of the nodes within the network in terms of its role, such as a transition node, in the same cluster or between clusters. In this paper, a new centrality model, based on random-paths betweenness centrality and applied on directed networks, is presented. Moreover, there is a centrality measure, based on random-paths betweenness centrality, that provides a classification of the nodes of undirected networks, that are able to reinforce dense communities according to their role. In that sense, the Betweenness centrality is a widely used measurement that quantifies the importance of a node in the information flow in a network. Centrality metrics are one of the most meaningful features in a large number of real-world network systems.
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