Stochastic Block Model
57 papers with code • 0 benchmarks • 0 datasets
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We show that, in a data-driven manner and without access to the underlying generative models, they can match or even surpass the performance of the belief propagation algorithm on binary and multi-class stochastic block models, which is believed to reach the computational threshold.
We show that this approach combines the performances of the non-backtracking operator, thus detecting clusters all the way down to the theoretical limit in the stochastic block model, with the computational, theoretical and memory advantages of real symmetric matrices.
We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters.
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Recently there has been increased interest in semi-supervised classification in the presence of graphical information.
Non-parametric approaches for analyzing network data based on exchangeable graph models (ExGM) have recently gained interest.
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected.
Scalable detection of statistically significant communities and hierarchies, using message-passing for modularity
We address this problem by using the modularity as a Hamiltonian at finite temperature, and using an efficient Belief Propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it.