… to clustering, based on deterministic analysis of random walks on the weighted graph
associated with the clustering … of inter-cluster edges (the so-called separators), and those of intra-…

… j-th component is the probability that a random walk originating at i will visit node j in its k-th …
, is defined as the probability that a random walk originating at s will reach t before returning …

… We propose here a measure of similarities between vertices based on random walks which
has several important advantages: it captures well the community structure in a network, it …

… , a random walk starts from an initial node, and based on a probability distribution, progresses
to neighboring nodes. Using … , we use the average number of steps a random walk takes …

… The random walk interpretation that we describe now will answer the first two questions
as well as give a better understanding of what spectral clustering is achieving. We shall not …

… We use restricted random walk probabilities to reinforce the … Our random walk model is
similar to the one proposed by … We use new probabilities derived from random walks as the …

… We propose here a measure of similarity between vertices based on random walks which
has several important advantages: it captures well the community structure in a network, it can …

… Bayesian inference of network properties as the network is explored by random walks [45]. …
by an ensemble of random walks. Intuitively, we utilize the fact that a random walk will, on …

… random walks and quantum walks. We first review the knowledge of classical random walks
and quantum walks, … algorithms based on quantum walks and classical random walks from …

… This work proposes a simple way to improve a clustering algorithm. The idea is to exploit a …
” (ECT) distance, based on a random walk model on a graph derived from the data. Using this …