Stochastic Block Model

57 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Supervised Community Detection with Line Graph Neural Networks

zhengdao-chen/GNN4CD ICLR 2019

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.

Spectral Clustering of Graphs with the Bethe Hessian

Jonas1312/CommunityDetection NeurIPS 2014

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.

Community Detection with Graph Neural Networks

joanbruna/GNN_community ICLR 2018

This graph inference task can be recast as a node-wise graph classification problem, and, as such, computational detection thresholds can be translated in terms of learning within appropriate models.

Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks

InterpretableClustering/InterpretableClustering 16 Dec 2020

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

opallab/icml-21-graph-conv 13 Feb 2021

Recently there has been increased interest in semi-supervised classification in the presence of graphical information.

Spectral redemption: clustering sparse networks

sa7oru/Social-Event-Magnitudes 24 Jun 2013

Spectral algorithms are classic approaches to clustering and community detection in networks.

Spectral Clustering for Divide-and-Conquer Graph Matching

lichen11/LSGMcode 4 Oct 2013

We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs.

Stochastic blockmodel approximation of a graphon: Theory and consistent estimation

airoldilab/SBA NeurIPS 2013

Non-parametric approaches for analyzing network data based on exchangeable graph models (ExGM) have recently gained interest.

Efficiently inferring community structure in bipartite networks

sayali-sonawane/LinkPrediction 12 Mar 2014

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

bwalker1/ModularityBP_Cpp 23 Mar 2014

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.