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
Latest papers
Maximum Likelihood Estimation on Stochastic Blockmodels for Directed Graph Clustering
This paper studies the directed graph clustering problem through the lens of statistics, where we formulate clustering as estimating underlying communities in the directed stochastic block model (DSBM).
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in graph partitioning mostly rely on eigen decompositions and thus, generally lack interpretability.
Hierarchical Position Embedding of Graphs with Landmarks and Clustering for Link Prediction
kmswin1/hplc
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HPLC leverages the positional information of nodes based on landmarks at various levels of hierarchy such as nodes' distances to landmarks, inter-landmark distances and hierarchical grouping of clusters.
Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering
drprojects/superpoint_transformer
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We introduce a highly efficient method for panoptic segmentation of large 3D point clouds by redefining this task as a scalable graph clustering problem.
Every Node is Different: Dynamically Fusing Self-Supervised Tasks for Attributed Graph Clustering
q086/dyfss
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In this paper, we propose to dynamically learn the weights of SSL tasks for different nodes and fuse the embeddings learned from different SSL tasks to boost performance.
Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis
Dynamic community detection methods often lack effective mechanisms to ensure temporal consistency, hindering the analysis of network evolution.
Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering
ZichenWen1/AHGFC
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Then we design an adaptive hybrid graph filter that is related to the homophily degree, which learns the node embedding based on the graph joint aggregation matrix.
Efficient High-Quality Clustering for Large Bipartite Graphs
A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely used to model real-world data, such as user-item purchase records, author-article publications, and biological interactions between drugs and proteins.
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization
Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology of the graph.
MeanCut: A Greedy-Optimized Graph Clustering via Path-based Similarity and Degree Descent Criterion
As the most typical graph clustering method, spectral clustering is popular and attractive due to the remarkable performance, easy implementation, and strong adaptability.
