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Specifically, eight complex network measures were selected from the literature, namely: assortativity, average local clustering coefficient, average degree, betweenness, average shortest path length, closeness, global clustering coefficient and eigenvector centrality.
In data classification, the use of complex networks allows the detection of structural and topological patterns related, for example, to the formation pattern.
In data classification, the use of complex networks allows the detection of structural and topological patterns related, for example, to the formation pattern.
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This paper presents a data classification technique in which low level and high level classifications are embedded in a unique scheme, i.e., the proposed ...
May 15, 2022 · The complex network improves the accuracy of classification results. •. The features of complex network reflect the dynamic information.
Oct 26, 2021 · There are many ways of constructing a complex network from tabular data. Here, I will describe a KNN Construction method that uses the closest ...
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PDF | This paper investigates the adoption of measures used to evaluate complex networks properties in the characterization of the complexity of data.
Jun 30, 2021 · Most multi-label learning (MLL) techniques perform classification by analyzing only the physical features of the data, which means they are ...
Sep 1, 2023 · Complex networks refer to large-scale graphs with nontrivial connection patterns [11–16]. This type of network is a suitable tool for ...
Jun 30, 2021 · In this paper, we extend that work by evaluating different combinations of four complex networks measures, namely clustering coefficient, ...