Cyberspace Skeleton Map Visualization Considering Node Quality Correction and Spatial Distribution Characteristics

Nodes are important elements of the cyberspace skeleton map visualization process. However, the quality parameters of the node importance index and topological potential index are difficult to obtain, and skeleton map visualization rarely accounts for the spatial distribution characteristics of nodes. The index synthesis and cluster distribution methods are adopted to solve these problems in this paper. The results are as follows: (1) According to the SIR propagation model, the maximum numbers of recoveries and infections for both the ARPA network and social network equal the TPDomiH maximum, and the TPDomiH index has the largest correlation coefficient. All the results show that the proposed TPDomiH index has certain advantages. (2) Regarding the center, the clustering results obtained for a social network are almost unchanged, whereas the original results exhibit large changes. For the center of gravity, the clustering results decrease gradually. The differences relative to the original results are small. With respect to the information entropy and the maximum amount of geometric information, the clustering results are larger than the original results. As the retention ratio increases, all the differences between the clustering results and the original results gradually narrow. These results indicate that the cyberspace skeleton map obtained after clustering is better than the original map. This research can provide a reference for the development of the field of cyberspace map visualization.