Abstract
Similarity calculation of large-scale graphs is essential in big data classification, sorting, and other work. However, when there are diverse attributes and the vertices are not ordered, the time and space complexity of similarity computation is often too high. This paper presents a unified representation comprehensive tensor (CT) of large-scale graphs with different specifications and attributes to save space. Besides, before approximation, the concept of a completely satisfied comprehensive tensor (CSCT) set is utilized to ensure the attribute consistency. Then, a spatial mapping (SM) method is proposed to approximate the similarity between two large-scale graphs. In this way, the computational memory is reduced to O(e+n), where e represents the edge number, and n represents the number of vertices. Moreover, the computational efficiency is improved a lot to O(e1+e2), in which e1 and e2 respectively represent the edge numbers of the two graphs for similarity calculation.