# Node Clustering

67 papers with code • 20 benchmarks • 15 datasets

## Libraries

Use these libraries to find Node Clustering models and implementations## Datasets

## Most implemented papers

# Attributed Graph Clustering: A Deep Attentional Embedding Approach

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

# Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity

In this paper, we propose Unified Graph Transformer Networks (UGT) that effectively integrate local and global structural information into fixed-length vector representations.

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

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

# MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

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

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

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

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

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.

# GraphLearner: Graph Node Clustering with Fully Learnable Augmentation

During the training procedure, we notice the distinct optimization goals for training learnable augmentors and contrastive learning networks.

# CONVERT:Contrastive Graph Clustering with Reliable Augmentation

To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).