Graph Clustering
145 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.
Benchmarks
These leaderboards are used to track progress in Graph Clustering
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Libraries
Use these libraries to find Graph Clustering models and implementationsDatasets
Pubmed
Cora
SNAP
Netzschleuder
97 synthetic datasets
Placenta
Most implemented papers
Variational Graph Auto-Encoders
tkipf/gae
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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
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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
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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
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ICML 2020
Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph.
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.
Dink-Net: Neural Clustering on Large Graphs
yueliu1999/awesome-deep-graph-clustering
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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
rflamary/POT
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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.
Ensemble Clustering for Graphs: Comparisons and Applications
We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering.
