Papers Graph Clustering
“Graph Clustering” 태그가 달린 논문 450편 · 필터 해제
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 ClusteringClustering Node Attributed Networks with Graph Neural Networks and Self Learning
Graph clustering - partitioning the node set of a graph into disjoint subsets that reflect some latent information - is a fundamental problem as it finds applications in a myriad of different scenarios. While this classi…
Graph ClusteringRecovering the Zipfian Distribution in Unsupervised Term Discovery
Unsupervised term discovery involves segmenting unlabelled speech into word- or syllable-like units and clustering these into a lexicon of candidate types. True lexicons follow a Zipfian distribution, yet the dominant ce…
Graph ClusteringA Nonmonotone Gradient-Based Algorithm for Symmetric Nonnegative Matrix Factorization and Graph Clustering
Symmetric nonnegative matrix factorization (Symmetric NMF) approximates a matrix as $WW^T$ with nonnegative rectangular factor $W$. It has broad applications in graph clustering and machine learning. In contrast to the N…
Graph ClusteringRobust Contrastive Graph Clustering with Adaptive Local-Global Integration
Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contrastive learning that have improved clustering via structural and att…
Contrastive LearningGraph ClusteringSupervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis
We present Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes Symmetric Nonnegative Matrix Tri-Factorization (SNMTF) from unsupervi…
Graph ClusteringPaMoSplat: Part-Aware Motion-Guided Gaussian Splatting for Dynamic Scene Reconstruction
Dynamic scene reconstruction represents a fundamental yet demanding challenge in computer vision and robotics. While recent progress in 3DGS-based methods has advanced dynamic scene modeling, obtaining high-fidelity rend…
Graph ClusteringBalanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems
Recommender systems have advanced markedly over the past decade by transforming each user/item into a dense embedding vector with deep learning models. At industrial scale, embedding tables constituted by such vectors of…
Graph ClusteringBeyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering
Attributed Graph Clustering (AGC) is a fundamental unsupervised task that partitions nodes into cohesive groups by jointly modeling structural topology and node attributes. While the advent of graph neural networks and s…
Self-Supervised LearningGraph ClusteringNode Role-Guided LLMs for Dynamic Graph Clustering
Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing how complex real-world systems evolve over time. However, existing methods are predominantly black-box models. They lac…
Graph ClusteringSPEGC: Continual Test-Time Adaptation via Semantic-Prompt-Enhanced Graph Clustering for Medical Image Segmentation
In medical image segmentation tasks, the domain gap caused by the difference in data collection between training and testing data seriously hinders the deployment of pre-trained models in clinical practice. Continual Tes…
Medical Image SegmentationTest-time AdaptationGraph ClusteringCTS-Bench: Benchmarking Graph Coarsening Trade-offs for GNNs in Clock Tree Synthesis
Graph Neural Networks (GNNs) are increasingly explored for physical design analysis in Electronic Design Automation, particularly for modeling Clock Tree Synthesis behavior such as clock skew and buffering complexity. Ho…
Computational EfficiencyGraph ClusteringIncluding Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models
This paper addresses graph learning in Gaussian Graphical Models (GGMs). In this context, data matrices often come with auxiliary metadata (e.g., textual descriptions associated with each node) that is usually ignored in…
Graph ClusteringGraph LearningSuperLocalMemory: Privacy-Preserving Multi-Agent Memory with Bayesian Trust Defense Against Memory Poisoning
We present SuperLocalMemory, a local-first memory system for multi-agent AI that defends against OWASP ASI06 memory poisoning through architectural isolation and Bayesian trust scoring, while personalizing retrieval thro…
Graph ClusteringBridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering
Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph-structured data. Despite its significance in industrial app…
Graph ClusteringFraud Detection