Topological versus spectral properties of random geometric graphs

Author(s)

Aguilar-Sanchez, R. ; Mendez-Bermudez, J. A. ; Rodrigues, Francisco A. ; Sigarreta, Jose M.

• Document type: Journal article
• Source: PHYSICAL REVIEW E; v. 102, n. 4, p. 8-pg., 2020-10-16.

Abstract

In this work we perform a detailed statistical analysis of topological and spectral properties of random geometric graphs (RGGs), a graph model used to study the structure and dynamics of complex systems embedded in a two-dimensional space. RGGs, G(n, l), consist of n vertices uniformly and independently distributed on the unit square, where two vertices are connected by an edge if their Euclidian distance is less than or equal to the connection radius l is an element of [0, root 2]. To evaluate the topological properties of RGGs we chose two well-known topological indices, the Randic index R(G) and the harmonic index H(G). We characterize the spectral and eigenvector properties of the corresponding randomly weighted adjacency matrices by the use of random matrix theory measures: the ratio between consecutive eigenvalue spacings, the inverse participation ratios, and the information or Shannon entropies S(G). First, we review the scaling properties of the averaged measures, topological and spectral, on RGGs. Then we show that (i) the averaged-scaled indices, < R(G)> and < H(G)>, are highly correlated with the average number of nonisolated vertices < V-x(G)>; and (ii) surprisingly, the averaged-scaled Shannon entropy < S(G)> is also highly correlated with < V-x(G)>. Therefore, we suggest that very reliable predictions of eigenvector properties of RGGs could be made by computing topological indices. (AU)

• FAPESP's process: 19/06931-2 - Random matrix theory approach to complex networks
• Grantee: Francisco Aparecido Rodrigues
• Support Opportunities: Research Grants - Visiting Researcher Grant - International