Graph Clustering
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Benchmarks
Citeseer
Cora
Pubmed
Biase et al
Bozec et al
Deng et al
Goolam et al
Pollen et al
Treutlein et al
Yan et al
Most implemented
Variational Graph Auto-Encoders
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Spectral Clustering with Graph Neural Networks for Graph Pooling
Adversarially Regularized Graph Autoencoder for Graph Embedding
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Ensemble Clustering for Graphs
Papers
Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph
This work addresses community detection in temporal networks through GPU-accelerated extensions of spectral clustering and modularity-based algorithms originally designed for static graphs. Built on the NVIDIA RAPIDS eco…
Community DetectionGraph ClusteringRHEA: Reliability-Harmonized Reconstruction and Assignment for Robust Multimodal-Attributed Graph Clustering
Multimodal-attributed graphs (MAGs), whose nodes carry heterogeneous attributes such as text and images over a relational structure, have become a fundamental substrate for label-free entity grouping tasks, including com…
Graph ClusteringBreaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated promising performance, existing methods o…
Contrastive LearningGraph ClusteringscKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering
Single-cell RNA sequencing (scRNA-seq) clustering is essential for identifying cell types, but high dimensionality, sparsity, dropout, and technical noise hinder robust expression representation and cell graph constructi…
Contrastive LearningGraph ClusteringBridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning
Attributed graph clustering partitions nodes by jointly exploiting node attributes and graph topology. It remains challenging due to attribute heterogeneity and representation degradation during graph learning. Real-worl…
Graph Representation LearningGraph ClusteringGraph LearningDYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning
Large Language Models (LLMs) struggle to incorporate new knowledge without forgetting or costly retraining. We propose DYNA, a lightweight framework that augments a frozen LLM with a temporal knowledge graph where events…
Knowledge GraphsGraph Clustering