Node Clustering

46 papers with code • 7 benchmarks • 8 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.

Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

cmavro/Graph-InfoClust-GIC 15 Sep 2020

Motivated by this observation, we propose a graph representation learning method called Graph InfoClust (GIC), that seeks to additionally capture cluster-level information content.

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.

NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning

zwt233/NAFS 17 Jun 2022

First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.

Search Efficient Binary Network Embedding

daokunzhang/binaryne 14 Jan 2019

In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network.

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.