Structural Relationships Extraction of Knowledge Networks Based on Eigen Decomposition 后印本

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.