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 with no code
Incorporating Higher-order Structural Information for Graph Clustering
In recent years, graph convolutional network (GCN) has emerged as a powerful tool for deep clustering, integrating both graph structural information and node attributes.
Graph learning methods to extract empathy supporting regions in a naturalistic stimuli fMRI
Our novel processing pipeline applies graph learning methods to whole-brain timeseries signals, incorporating high-pass filtering, voxel-level clustering, and windowed graph learning with a sparsity-based approach.
Provable Filter for Real-world Graph Clustering
Motivated by this finding, we construct two graphs that are highly homophilic and heterophilic, respectively.
Randomized Algorithms for Symmetric Nonnegative Matrix Factorization
The second algorithm uses randomized leverage score sampling to approximately solve constrained least squares problems.
Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks
This improves the decentralization, coordination, scalability, and efficiency of FL in large-scale UAV networks.
Overlap-aware End-to-End Supervised Hierarchical Graph Clustering for Speaker Diarization
Speaker diarization, the task of segmenting an audio recording based on speaker identity, constitutes an important speech pre-processing step for several downstream applications.
MaskClustering: View Consensus based Mask Graph Clustering for Open-Vocabulary 3D Instance Segmentation
Through iterative clustering of masks showing high view consensus, we generate a series of clusters, each representing a distinct 3D instance.
Masked AutoEncoder for Graph Clustering without Pre-defined Cluster Number k
However, for existing graph autoencoder clustering algorithms based on GCN or GAT, not only do they lack good generalization ability, but also the number of clusters clustered by such autoencoder models is difficult to determine automatically.
VSR-Net: Vessel-like Structure Rehabilitation Network with Graph Clustering
Based on this, we propose a novel Vessel-like Structure Rehabilitation Network (VSR-Net) to rehabilitate subsection ruptures and improve the model calibration based on coarse vessel-like structure segmentation results.
A low-rank non-convex norm method for multiview graph clustering
This study introduces a novel technique for multi-view clustering known as the "Consensus Graph-Based Multi-View Clustering Method Using Low-Rank Non-Convex Norm" (CGMVC-NC).
