The points of a graph G will form clusters as a result of a flow process. Initially, points i of G own resources x(i) which are i.i.d. random real numbers. Afterwards, resources flow between points, but always from a point to a neighbor that has accumulated a larger total resource. Thus points with small resource tend to lose it and points with large resource tend to gain. Eventually the flow stops with only two kinds of points, nulls with no resource left and absorbers with such large resource that no neighbor can take it. The final resource at an absorber is a sum of certain initial resources x(i), and the corresponding points i form one cluster. Analytical results are obtainable when G is the chain of integer points on the line. P...
In this paper we consider the clustering coefficient, and clustering function in a random graph mode...
Algorithms based on simulating stochastic flows are a sim-ple and natural solution for the problem o...
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set ...
Coffman et al. (1991) have introduced a flow process in graphs, where each vertex gets an initial ra...
We study a discrete‐time resource flow in \input amssym ${\Bbb Z}^d $ where wealthier vertices attra...
Most graph decomposition procedures seek to partition a graph into disjoint sets of vertices. Motiva...
Graph clustering is a fundamental computational problem with a number of applications in algorithm d...
International audienceMotivated by the analysis of social networks, we study a model of random netwo...
This dissertation studies two important algorithmic problems on networks : graph diffusion and clust...
We present random sampling algorithms that with probability at least 1 − δ compute a (1 ± ɛ)approxim...
AbstractGiven a random graph, we investigate the occurrence of subgraphs especially rich in edges. S...
We consider the problem of clustering in its most basic form where only a local metric on the data s...
AbstractThe random cluster model is an intriguing stochastic model on the vertices and edges of a gr...
We analyze a distributed variation on the P´olya urn process in which a network of tiny artifacts ma...
Abstract—Clustering of a graph is the task of grouping its nodes in such a way that the nodes within...
In this paper we consider the clustering coefficient, and clustering function in a random graph mode...
Algorithms based on simulating stochastic flows are a sim-ple and natural solution for the problem o...
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set ...
Coffman et al. (1991) have introduced a flow process in graphs, where each vertex gets an initial ra...
We study a discrete‐time resource flow in \input amssym ${\Bbb Z}^d $ where wealthier vertices attra...
Most graph decomposition procedures seek to partition a graph into disjoint sets of vertices. Motiva...
Graph clustering is a fundamental computational problem with a number of applications in algorithm d...
International audienceMotivated by the analysis of social networks, we study a model of random netwo...
This dissertation studies two important algorithmic problems on networks : graph diffusion and clust...
We present random sampling algorithms that with probability at least 1 − δ compute a (1 ± ɛ)approxim...
AbstractGiven a random graph, we investigate the occurrence of subgraphs especially rich in edges. S...
We consider the problem of clustering in its most basic form where only a local metric on the data s...
AbstractThe random cluster model is an intriguing stochastic model on the vertices and edges of a gr...
We analyze a distributed variation on the P´olya urn process in which a network of tiny artifacts ma...
Abstract—Clustering of a graph is the task of grouping its nodes in such a way that the nodes within...
In this paper we consider the clustering coefficient, and clustering function in a random graph mode...
Algorithms based on simulating stochastic flows are a sim-ple and natural solution for the problem o...
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set ...