Graph Clustering

82 papers with code • 10 benchmarks • 17 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.

Source: Clustering for Graph Datasets via Gumbel Softmax


Use these libraries to find Graph Clustering models and implementations

Most implemented papers

Variational Graph Auto-Encoders

tkipf/gae 21 Nov 2016

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

google-research/google-research KDD 2019

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].

Adversarially Regularized Graph Autoencoder for Graph Embedding

Ruiqi-Hu/ARGA 13 Feb 2018

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

Spectral Clustering with Graph Neural Networks for Graph Pooling

FilippoMB/Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling ICML 2020

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

Ensemble Clustering for Graphs

ftheberge/graph-partition-and-measures 14 Sep 2018

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

Graph-Bert: Only Attention is Needed for Learning Graph Representations

jwzhanggy/Graph-Bert 15 Jan 2020

We have tested the effectiveness of GRAPH-BERT on several graph benchmark datasets.

Optimal Transport for structured data with application on graphs

rflamary/POT 23 May 2018

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

Hierarchical Graph Clustering using Node Pair Sampling

tbonald/paris 5 Jun 2018

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

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

nlpub/watset-java CL 2019

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.

Ensemble Clustering for Graphs: Comparisons and Applications

ftheberge/graph-partition-and-measures 19 Mar 2019

We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering.