Node Clustering

33 papers with code • 7 benchmarks • 7 datasets

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Use these libraries to find Node Clustering models and implementations

Most implemented papers

Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks

benedekrozemberczki/karateclub arXiv 2018

It is not straightforward to integrate the content of each node in the current state-of-the-art network embedding methods.

Attributed Graph Clustering: A Deep Attentional Embedding Approach

Tiger101010/DAEGC 15 Jun 2019

Graph clustering is a fundamental task which discovers communities or groups in networks.

MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

cynricfu/MAGNN 5 Feb 2020

A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.

Simple Spectral Graph Convolution

allenhaozhu/SSGC ICLR 2021

Our spectral analysis shows that our simple spectral graph convolution used in S^2GC is a low-pass filter which partitions networks into a few large parts.

Asymptotics of Network Embeddings Learned via Subsampling

aday651/embed-asym-experiments 6 Jul 2021

We prove, under the assumption that the graph is exchangeable, that the distribution of the learned embedding vectors asymptotically decouples.

Attributed Network Embedding via Subspace Discovery

daokunzhang/attri2vec 14 Jan 2019

In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders

MysteryVaibhav/DW-GAE 12 Aug 2019

Node embeddings have become an ubiquitous technique for representing graph data in a low dimensional space.

Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks

deezer/linear_graph_autoencoders 2 Oct 2019

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.

Heterogeneous Deep Graph Infomax

YuxiangRen/Heterogeneous-Deep-Graph-Infomax 19 Nov 2019

The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.

Simple and Effective Graph Autoencoders with One-Hop Linear Models

deezer/linear_graph_autoencoders 21 Jan 2020

Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.