# Node Clustering

46 papers with code • 7 benchmarks • 8 datasets

## Libraries

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

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

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

# 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.

# Search Efficient Binary Network Embedding

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

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

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