Subjects: Library Science，Information Science >> Library Science submitted time 2023-08-26 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] Exploring the clustering evolution in the process of domain knowledge development can help to reveal the characteristics and rules of knowledge clustering, this is great significance to master the clustering rules of correlation knowledge in the development and evolution process.[Method/process] Based on the idea of complex network, this paper constructed the time series domain knowledge networks in accordance with the occurred-value of tags adjacency relation. That is, according to the network motif theory, this paper dynamically tracked and analyzed the domain knowledge networks by the analysis method of network clustering coefficient. Then, by combining with the network density, the characteristic path length, the node degree value, the triadic closure and other indicators, this article analyzed the clustering evolution in the process of domain knowledge development from random factors, degree correlation, and adjacent correlation.[Result/conclusion] The results show:①Domain knowledge in the development process always keeps a higher clustering. ②The clustering of domain knowledge includes both randomness and structuration (non-randomness). ③The dynamic status of domain knowledge clustering evolves between small-world network and scale-free network waveringly. ④The clustering status of domain knowledge shows a certain difference between the whole network and local nodes.
Subjects: Library Science，Information Science >> Library Science submitted time 2023-07-26 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] The effective identification and extraction of structural relationships in knowledge networks helps to detect the topology of knowledge networks and their evolution patterns from a wide range of data. [Method/process] This article proposes a method for extracting structural relationships in knowledge networks based on eigen decomposition of adjacency matrix. Using the real data, the eigen decomposition method and traditional correlation frequency method are compared and analyzed from static structural relationships extraction and dynamic structure evolution, and compared with the pathfinder algorithm. The validity of structural relationships extraction of knowledge networks based on eigen decomposition method is verified. [Result/conclusion] The research results show:the eigen decomposition method can identify the main component information in the original knowledge networks, the method can accurately identify the low-frequency correlations that are important to the global topology of the networks, and the extraction method is flexible and free.
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