graphs-5

Stochastic Block Model

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Supervised Community Detection with Line Graph Neural Networks

Joan Bruna, Lisha Li, Zhengdao Chen•Mon May 22 2017

This work presents a novel family of Graph Neural Networks (GNNs) for solving community detection problems in a supervised learning setting and shows 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.

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Spectral Clustering of graphs with the Bethe Hessian

Alaa Saade, Florent Krzakala, L. Zdeborová•Fri Jun 06 2014

This work proposes to use a simpler object, a symmetric real matrix known as the Bethe Hessian operator, or deformed Laplacian, and shows 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.

162 0

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One-Hot Graph Encoder Embedding

Cencheng Shen, Qizhe Wang, C. Priebe•Sun Sep 26 2021

A lightning fast graph embedding method called one-hot graph encoder embedding is proposed, which has a linear computational complexity and the capacity to process billions of edges within minutes on standard PC — making it an ideal candidate for huge graph processing.

40 0

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Latent space models for multiplex networks with shared structure

Peter Macdonald, E. Levina, Ji Zhu•Sun Dec 27 2020

A new latent space model for multiplex networks: multiple, heterogeneous networks observed on a shared node set that establishes identifiability, develops a fitting procedure using convex optimization in combination with a nuclear norm penalty, and proves a guarantee of recovery for the latent positions as long as there is sufficient separation between the shared and the individual latent subspaces.

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Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks

Yuhang Yao, Carlee Joe-Wong•Tue Dec 15 2020

This work describes the optimal decay rate for each cluster and proposes a clustering method that achieves almost exact recovery of the true clusters, and demonstrates the efficacy of the clustering algorithm with optimized decay rates on simulated graph data.

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Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization

Aseem Baranwal, K. Fountoulakis, Aukosh Jagannath•Fri Feb 12 2021

It is shown that graph convolution extends the regime in which the data is linearly separable by a factor of roughly $1/\sqrt{D}$, where $D$ is the expected degree of a node, as compared to the mixture model data on its own.

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Spectral clustering for divide-and-conquer graph matching

J. Vogelstein, C. Priebe, V. Lyzinski, D. Sussman, D. E. Fishkind, H. Pao, Li Chen, Youngser Park•Thu Oct 03 2013

This work presents a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs, showing up to a factor of 8 improvement in runtime with minimal sacrifice in accuracy.

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Stochastic blockmodel approximation of a graphon: Theory and consistent estimation

E. Airoldi, Thiago B. Costa, Stanley H. Chan•Wed Nov 06 2013

This paper proposes a computationally efficient procedure to estimate a graphon from a set of observed networks generated from it based on a stochastic blockmodel approximation (SBA) of the graphon, and shows that the estimation error vanishes as the size of thegraph approaches infinity.

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Efficiently inferring community structure in bipartite networks

D. Larremore, A. Clauset, Abigail Z. Jacobs•Tue Mar 11 2014

This work formulates a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to k-partite networks and demonstrates this model's ability to efficiently and accurately find community structure in synthetic bipartites with known structure and in real-world bipartITE networks with unknown structure.

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