We analyze a distributed variation on the Pólya urn process in which a network of tiny artifacts manages the individual urns. Neighboring urns interact by repeatedly adding the same colored ball based on previous random choices. We discover that the process rapidly converges to a definitive random ratio between the colors in every urn. Moreover, the rate of convergence of the process at a given node depends on the global topology of the network. In particular, the same ratio appears for the case of complete communication graphs. Surprisingly, this effortless random process supports useful applications, such as clustering and computation of pseudo-geometric coordinate. We present numerical studies that validate our theoretical predictions
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
This dissertation develops an inferential framework for a highly non-parametric class of network mod...
Random graphs with power-law degrees can model scale-free networks as sparse topologies with strong ...
We analyze a distributed variation on the Pólya urn process in which a network of tiny artifacts man...
We analyze a distributed variation on the P´olya urn process in which a network of tiny artifacts ma...
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the ...
International audienceThe stochastic models investigated in this paper describe the evolution of a s...
International audienceMotivated by the analysis of social networks, we study a model of random netwo...
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
Recently, graph matching algorithms have been successfully applied to the problem of network de-anon...
We present an algorithm for generating random networks with arbitrary degree distribution and cluste...
We develop a full theoretical approach to clustering in complex networks. A key concept is introduce...
This dissertation studies two important algorithmic problems on networks : graph diffusion and clust...
We consider a system of N two-colors urns in which the reinforcement of each urn depends also on the...
Consider a finite undirected graph and place an urn with balls of two colours at each vertex. At eve...
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
This dissertation develops an inferential framework for a highly non-parametric class of network mod...
Random graphs with power-law degrees can model scale-free networks as sparse topologies with strong ...
We analyze a distributed variation on the Pólya urn process in which a network of tiny artifacts man...
We analyze a distributed variation on the P´olya urn process in which a network of tiny artifacts ma...
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the ...
International audienceThe stochastic models investigated in this paper describe the evolution of a s...
International audienceMotivated by the analysis of social networks, we study a model of random netwo...
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
Recently, graph matching algorithms have been successfully applied to the problem of network de-anon...
We present an algorithm for generating random networks with arbitrary degree distribution and cluste...
We develop a full theoretical approach to clustering in complex networks. A key concept is introduce...
This dissertation studies two important algorithmic problems on networks : graph diffusion and clust...
We consider a system of N two-colors urns in which the reinforcement of each urn depends also on the...
Consider a finite undirected graph and place an urn with balls of two colours at each vertex. At eve...
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
This dissertation develops an inferential framework for a highly non-parametric class of network mod...
Random graphs with power-law degrees can model scale-free networks as sparse topologies with strong ...