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GLASS: A Graph Laplacian Autoencoder with Subspace Clustering Regularization for Graph Clustering
Graph clustering is an important unsupervised learning task in complex network analysis and its latest progress mainly relies on a graph autoencoder...
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A faster deep graph clustering network based on dynamic graph weight update mechanism
Deep graph clustering has attracted considerable attention for its potential in handling complex graph-structured data. However, existing approaches...
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Investigation of graph-based clustering approaches along with graph neural networks for modeling armed conflict in Bangladesh
Determining fatality rates—a critical component of conflict analysis and comprehending the dynamics of armed conflict in Bangladesh are the main...
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Index-free triangle-based graph local clustering
Motif-based graph local clustering (MGLC) is a popular method for graph mining tasks due to its various applications. However, the traditional...
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Clustering using graph convolution networks
Graph convolution networks (GCNs) have emerged as powerful approaches for semi-supervised classification of attributed graph data. In this paper, we...
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One-step graph-based incomplete multi-view clustering
Existing graph-based incomplete multi-view clustering methods mainly adopt the three-step strategy, i.e., graph completion, graph fusion (consensus...
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Deep graph clustering via mutual information maximization and mixture model
Attributed graph clustering or community detection which learns to cluster the nodes of a graph is a challenging task in graph analysis. Recently...
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Self-supervised graph clustering via attention auto-encoder with distribution specificity
Graph clustering, an essential unsupervised learning task in data mining, has garnered significant attention in recent years. With the advent of deep...
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Consensus Affinity Graph Learning via Structure Graph Fusion and Block Diagonal Representation for Multiview Clustering
Learning a robust affinity graph is fundamental to graph-based clustering methods. However, some existing affinity graph learning methods have...
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A weighted multi-view clustering via sparse graph learning
Multi-view clustering considers the diversity of different views and fuses these views to produce a more accurate and robust partition than...
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Deep Self-Supervised Attributed Graph Clustering for Social Network Analysis
Deep graph clustering is an unsupervised learning task that divides nodes in a graph into disjoint regions with the help of graph auto-encoders....
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Graph analysis using a GPU-based parallel algorithm: quantum clustering
The article introduces a new method for applying Quantum Clustering to graph structures. Quantum Clustering (QC) is a density-based unsupervised...
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Deep graph-level clustering using pseudo-label-guided mutual information maximization network
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the...
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PECC: parallel expansion based on clustering coefficient for efficient graph partitioning
In the pursuit of graph processing performance, graph partitioning, as a crucial preprocessing step, has been widely concerned. Based on an in-depth...
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Consensus similarity graph construction for clustering
A similarity graph represents the local characteristics of a data set, and it is used as input to various clustering methods including spectral,...
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Graph attention autoencoder model with dual decoder for clustering single-cell RNA sequencing data
Single-cell ribonucleic acid sequencing (scRNA-seq) allows researchers to study cell heterogeneity and diversity at the individual cell level. Cell...
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Integrated Heterogeneous Graph Attention Network for Incomplete Multi-modal Clustering
Incomplete multi-modal clustering (IMmC) is challenging due to the unexpected missing of some modalities in data. A key to this problem is to explore...
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Graph-Enforced Neural Network for Attributed Graph Clustering
Graph clustering aims to discover cluster structures in graphs. This task becomes more challenging when each node in the graph is associated with an...
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A self-adaptive graph-based clustering method with noise identification
Graph-based clustering methods offer competitive performance in dealing with complex and nonlinear data patterns. The outstanding characteristic of...
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An efficient graph embedding clustering approach for heterogeneous network
Recently, the analysis of heterogeneous networks has become more popular due to the growing number of social networks. These networks are capable of...
