Stochastic Block Model
77 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Supervised Community Detection with Line Graph Neural Networks
zhengdao-chen/GNN4CD
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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
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
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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
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We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters.
Latent space models for multiplex networks with shared structure
Here we propose a new latent space model for multiplex networks: multiple, heterogeneous networks observed on a shared node set.
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.
One-Hot Graph Encoder Embedding
In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding.
Spectral Clustering for Divide-and-Conquer Graph Matching
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
Non-parametric approaches for analyzing network data based on exchangeable graph models (ExGM) have recently gained interest.
Efficiently inferring community structure in bipartite networks
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.
