Graph Clustering
146 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
Parallel Graph Partitioning for Complex Networks
This paper addresses this problem by parallelizing and adapting the label propagation technique originally developed for graph clustering.
Spectral Theory of Unsigned and Signed Graphs. Applications to Graph Clustering: a Survey
This is a survey of the method of graph cuts and its applications to graph clustering of weighted unsigned and signed graphs.
Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering
One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.
AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.
When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum
The study of complex systems benefits from graph models and their analysis.
Residual Gated Graph ConvNets
xbresson/spatial_graph_convnets
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ICLR 2018
In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks.
Affinity Clustering: Hierarchical Clustering at Scale
In particular, we propose affinity, a novel hierarchical clustering based on Boruvka's MST algorithm.
A Streaming Algorithm for Graph Clustering
We introduce a novel algorithm to perform graph clustering in the edge streaming setting.
Learning Networks from Random Walk-Based Node Similarities
In this work we consider a privacy threat to a social network in which an attacker has access to a subset of random walk-based node similarities, such as effective resistances (i. e., commute times) or personalized PageRank scores.
Memetic Graph Clustering
It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches.
