By assigning a probability measure via the spectrum of the normalized Laplacian to each graph and using Lp Wasserstein distances between probability measures, we define the corresponding spectral distances dp on the set of all graphs. This approach can even be extended to measuring the distances between infinite graphs. We prove that the diameter of the set of graphs, as a pseudo-metric space equipped with d1, is one. We further study the behavior of d1 when the size of graphs tends to infinity by interlacing inequalities aiming at exploring large real networks. A monotonic relation between d1 and the evolutionary distance of biological networks is observed in simulations
summary:Let $G$ be a connected graph with vertex set $V(G)=\{v_{1},v_{2},\ldots ,v_{n}\}$. The dista...
In this paper we study the diameter of the random graph $G(n,p)$, i.e., the the largest finite dista...
The theme of this paper is the study of typical distances in a ran-dom graph model that was introduc...
AbstractBy assigning a probability measure via the spectrum of the normalized Laplacian to each grap...
By assigning a probability measure via the spectrum of the nor-malized Laplacian to each graph and u...
In this thesis, we explore applications of spectral graph theory to the analysis of complex datasets...
How is the shape of a graph captured by the way heat diffuses between its nodes? The Laplacian Expon...
Abstract. The investigation of the spectral distances of graphs that started in [3] (I. Jovanović, ...
pre-printWe propose a novel difference metric, called the graph diffusion distance (GDD), for quanti...
We define a new family of similarity and distance measures on graphs, and explore their theoretical ...
AbstractLet λ1(G)⩾λ2(G)⩾⋯⩾λn(G) be the adjacency spectrum of a graph G on n vertices. The spectral d...
The distance for a pair of vertices in a graph G is the length of the shortest path between them. Th...
In this paper, we investigate various algebraic and graph theoretic properties of the distance matri...
The spectrum of a graph usually provides a lot of information about its combinatorial structure. Mor...
Network complexity has been studied for over half a century and has found a wide range of applicatio...
summary:Let $G$ be a connected graph with vertex set $V(G)=\{v_{1},v_{2},\ldots ,v_{n}\}$. The dista...
In this paper we study the diameter of the random graph $G(n,p)$, i.e., the the largest finite dista...
The theme of this paper is the study of typical distances in a ran-dom graph model that was introduc...
AbstractBy assigning a probability measure via the spectrum of the normalized Laplacian to each grap...
By assigning a probability measure via the spectrum of the nor-malized Laplacian to each graph and u...
In this thesis, we explore applications of spectral graph theory to the analysis of complex datasets...
How is the shape of a graph captured by the way heat diffuses between its nodes? The Laplacian Expon...
Abstract. The investigation of the spectral distances of graphs that started in [3] (I. Jovanović, ...
pre-printWe propose a novel difference metric, called the graph diffusion distance (GDD), for quanti...
We define a new family of similarity and distance measures on graphs, and explore their theoretical ...
AbstractLet λ1(G)⩾λ2(G)⩾⋯⩾λn(G) be the adjacency spectrum of a graph G on n vertices. The spectral d...
The distance for a pair of vertices in a graph G is the length of the shortest path between them. Th...
In this paper, we investigate various algebraic and graph theoretic properties of the distance matri...
The spectrum of a graph usually provides a lot of information about its combinatorial structure. Mor...
Network complexity has been studied for over half a century and has found a wide range of applicatio...
summary:Let $G$ be a connected graph with vertex set $V(G)=\{v_{1},v_{2},\ldots ,v_{n}\}$. The dista...
In this paper we study the diameter of the random graph $G(n,p)$, i.e., the the largest finite dista...
The theme of this paper is the study of typical distances in a ran-dom graph model that was introduc...