Add to ORKGAbstract. This work provides the first detailed investigation of the dis-turbed diffusion scheme FOS/C introduced in [17] as a type of diffu-sion distance measure within a graph partitioning framework related to Lloyd's k-means algorithm [14]. After outlining connections to distance measures proposed in machine learning, we show that FOS/C can be related to random walks despite its disturbance. Its convergence proper-ties regarding load distribution and edge flow characterization are exam-ined on two different graph classes, namely torus graphs and distance-transitive graphs (including hypercubes), representatives of which are frequently used as interconnection networks
Time plays an essential role in the diffusion of information, influence and disease over networks. I...
<p>Properties of the first principal network are given for each of the diffusion data sets analysed....
the date of receipt and acceptance should be inserted later Abstract In this paper we study the prev...
Abstract—We introduce the diffusion and superposition dis-tances as two metrics to compare signals s...
This article proposes the augmentation of the adjacency model of networks for graph signal processin...
In a connected graph, nodes can be characterised locally (with their degree k) or globally (e.g. wit...
This article proposes the augmentation of the adjacency model of networks for graph signal processin...
© 2018 Elsevier Ltd In network embedding, random walks play a fundamental role in preserving network...
pre-printWe propose a novel difference metric, called the graph diffusion distance (GDD), for quanti...
With the increase in data acquisition and storage capabilities, developing efficient methods for pro...
We propose a novel difference metric, called the graph diffusion dis-tance (GDD), for quantifying th...
We consider the problem of determining the proportion of edges that are discovered in an Erdos-Rényi...
How is the shape of a graph captured by the way heat diffuses between its nodes? The Laplacian Expon...
Complex networks are characterized by latent geometries induced by their topology or by the dynamics...
In a connected graph, nodes can be characterised locally (with their degree k) or globally (e.g. wit...
Time plays an essential role in the diffusion of information, influence and disease over networks. I...
<p>Properties of the first principal network are given for each of the diffusion data sets analysed....
the date of receipt and acceptance should be inserted later Abstract In this paper we study the prev...
Abstract—We introduce the diffusion and superposition dis-tances as two metrics to compare signals s...
This article proposes the augmentation of the adjacency model of networks for graph signal processin...
In a connected graph, nodes can be characterised locally (with their degree k) or globally (e.g. wit...
This article proposes the augmentation of the adjacency model of networks for graph signal processin...
© 2018 Elsevier Ltd In network embedding, random walks play a fundamental role in preserving network...
pre-printWe propose a novel difference metric, called the graph diffusion distance (GDD), for quanti...
With the increase in data acquisition and storage capabilities, developing efficient methods for pro...
We propose a novel difference metric, called the graph diffusion dis-tance (GDD), for quantifying th...
We consider the problem of determining the proportion of edges that are discovered in an Erdos-Rényi...
How is the shape of a graph captured by the way heat diffuses between its nodes? The Laplacian Expon...
Complex networks are characterized by latent geometries induced by their topology or by the dynamics...
In a connected graph, nodes can be characterised locally (with their degree k) or globally (e.g. wit...
Time plays an essential role in the diffusion of information, influence and disease over networks. I...
<p>Properties of the first principal network are given for each of the diffusion data sets analysed....
the date of receipt and acceptance should be inserted later Abstract In this paper we study the prev...