# Graph Clustering

149 papers with code • 10 benchmarks • 18 datasets

**Graph Clustering** is the process of grouping the nodes of the graph into clusters, taking into account the edge structure of the graph in such a way that there are several edges within each cluster and very few between clusters. Graph Clustering intends to partition the nodes in the graph into disjoint groups.

## Libraries

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

## Most implemented papers

# Variational Graph Auto-Encoders

We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE).

# Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].

# Spectral Clustering with Graph Neural Networks for Graph Pooling

Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph.

# Adversarially Regularized Graph Autoencoder for Graph Embedding

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.

# Hierarchical Graph Clustering using Node Pair Sampling

We present a novel hierarchical graph clustering algorithm inspired by modularity-based clustering techniques.

# Ensemble Clustering for Graphs

We also illustrate how the ensemble obtained with ECG can be used to quantify the presence of community structure in the graph.

# Attributed Graph Clustering: A Deep Attentional Embedding Approach

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

# Dink-Net: Neural Clustering on Large Graphs

Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner.

# Optimal Transport for structured data with application on graphs

This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space.

# Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction

We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains.