Graph Clustering Research Articles

EBMGC-GNF: Efficient Balanced Multi-View Graph Clustering via Good Neighbor Fusion.

Exploiting consistent structure from multiple graphs is vital for multi-view graph clustering. To achieve this goal, we propose an Efficient Balanced Multi-view Graph Clustering via Good Neighbor Fusion (EBMGC-GNF) model which comprehensively extracts credible consistent neighbor information from multiple views by designing a Cross-view Good Neighbors Voting module. Moreover, a novel balanced regularization term based on p-power function is introduced to adjust the balance property of clusters, which helps the model adapt to data with different distributions. To solve the optimization problem of EBMGC-GNF, we transform EBMGC-GNF into an efficient form with graph coarsening method and optimize it based on accelareted coordinate descent algorithm. In experiments, extensive results demonstrate that, in the majority of scenarios, our proposals outperform state-of-the-art methods in terms of both effectiveness and efficiency.

Fault Diagnosis Model of Hydraulic Motor Based on Fuzzy Neural Network

When the hydraulic motor fault occurs,it is not easy to be detected,and the leakage degree will gradually increase.In order toavoid bigger accidents causedby thehydraulic motor fault,the accident isexcluded in theembryonic stage,and the hydraulic motor fault prediction method based onfuzzy neural network isused topredict thehydraulic motor fault.The feature vector is output inthe global meanpooling layer,andthe feature vectormatrix between thehealth state feature vector library and the samples to be measured is constructed.The dynamic cluster graph isobtained by fuzzy clustering,so as to realize the fault diagnosis of thehydraulic motor.The results show that the accuracy of training set,verification set andtest set ishigher than99.8%.The accuracy of diagnosis classification is99.00%,which is better than othercomparison models.In this study,the number of training samples can be appropriately increased ordecreased according to thecurve complexity of the detection target,so as to improve thefeature extraction capability of the convolutional layer andincrease the classification accuracy.

Engineering of hyperentangled complex quantum networks

Abstract We propose a novel scheme to engineer the atomic hyperentangled cluster and ring graph states invoking cavity-QED technique for applicative relevance to quantum biology and quantum communications utilizing the complex quantum networks. These states are engineered using both external quantized momenta states and energy levels of neutral atoms under off-resonant and resonant Atomic Bragg Diffraction (ABD) technique. The study of dynamical capacity and potential efficiency have certainly enhanced the range of usefulness of these states. In order to assess the operational behavior of such states when subjected to a realistic noise environment has also been simulated, demonstrating long enough sustainability of the proposed states. Moreover, experimental feasibility of the proposed scheme has also been elucidated under the prevailing cavity-QED research scenario.

Optimization of Graph Clustering Inspired by Dynamic Belief Systems

Optimization of Graph Clustering Inspired by Dynamic Belief Systems

Joint Intra-view and Inter-view Enhanced Tensor Low-rank Induced Affinity Graph Learning

Graph-based and tensor-based multi-view clustering have gained popularity in recent years due to their ability to explore the relationship between samples. However, there are still several shortcomings in the current multi-view graph clustering algorithms. (1) Most previous methods only focus on the inter-view correlation, while ignoring the intra-view correlation. (2) They usually use the Tensor Nuclear Norm (TNN) to approximate the rank of tensors. However, while it has the same penalty for different singular values, the model cannot approximate the true rank of tensors well. To solve these problems in a unified way, we propose a new tensor-based multi-view graph clustering method. Specifically, we introduce the Enhanced Tensor Rank (ETR) minimization of intra-view and inter-view in the process of learning the affinity graph of each view. Compared with 10 state-of-the-art methods on 8 real datasets, the experimental results demonstrate the superiority of our method.

Wood trade regions of Russia: clustering approach development

Network analysis of the Russian timber industry was based on the information about timber transactions between Russian companies in 2020. The data were collected from the Unified State Automated Information System Accounting Timber and Transactions with It. The method of graph clustering was developed. The first step is clustering of the hole graph by Leiden algorithm. The second step is drawing of each Leiden created cluster in abstract space using Fruchterman–Reingold layout and extraction of clusters from layout using meanshift algorithm. Such approach helped us to divide many Leiden-created clusters into components. This algorithm has a problem of resolution limit. It tends to combine some medium and small clusters into large ones. Also, a special method was developed to limit the uncertainty of clustering – both Leiden algorithm and Fruchterman–Reingold layout are non-deterministic algorithms. Wood trade clusters vary in size and content. They are rather autonomous. The average share of intra-cluster trade is 89%. The modularity score of our partition is 0.863. Each cluster has been described using open sources from the Internet. The content of the clusters does not contradict the logic of the wood industry processes. The clusters create rather compact areas on the map – wood trade regions. Their borders often overlap with existing administrative boundaries. Five types of network structure of clusters were defined: vertical monocentric, vertical polycentric, horizontal, dendritic, simple. There are three factors of clustering in wood industry of Russia: production chains (4 types – pulp, plywood, lumber, chipboard chains), demand chains (redistribution of raw material between manufacturers within the cluster, accumulation of wood volumes for some enterprises outside the cluster, wood trade between traders within the cluster), common holder. We have shown that existing approaches to cluster detection based on location quotients often misrepresent economic networks.

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST’s capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

Enhancing ophthalmology medical record management with multi-modal knowledge graphs

The electronic medical record management system plays a crucial role in clinical practice, optimizing the recording and management of healthcare data. To enhance the functionality of the medical record management system, this paper develops a customized schema designed for ophthalmic diseases. A multi-modal knowledge graph is constructed, which is built upon expert-reviewed and de-identified real-world ophthalmology medical data. Based on this data, we propose an auxiliary diagnostic model based on a contrastive graph attention network (CGAT-ADM), which uses the patient’s diagnostic results as anchor points and achieves auxiliary medical record diagnosis services through graph clustering. By implementing contrastive methods and feature fusion of node types, text, and numerical information in medical records, the CGAT-ADM model achieved an average precision of 0.8563 for the top 20 similar case retrievals, indicating high performance in identifying analogous diagnoses. Our research findings suggest that medical record management systems underpinned by multimodal knowledge graphs significantly enhance the development of AI services. These systems offer a range of benefits, from facilitating assisted diagnosis and addressing similar patient inquiries to delving into potential case connections and disease patterns. This comprehensive approach empowers healthcare professionals to garner deeper insights and make well-informed decisions.

Kirchhoff index of some networks based on the cluster of graph

Abstract The Kirchhoff index represents the sum of resistance distances between all pairs of nodes in a network. It reflects the integrity and connectivity of the network. In this paper, we propose three classes of network models, all generated based on the cluster of graphs. We derive exact expressions for their Kirchhoff indices through an iterative methodology. Leveraging the derived formulas, we compared the Kirchhoff indices of the network families corresponding to three classes of networks generated under the same base graph. This enhances our evaluation of network characteristics, potentially serving as a critical tool in the practical design of networks.

Simple Contrastive Graph Clustering.

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to its promising performance. However, complicated data augmentations and time-consuming graph convolutional operations undermine the efficiency of these methods. To solve this problem, we propose a simple contrastive graph clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function. As to the architecture, our network includes two main parts, that is, preprocessing and network backbone. A simple low-pass denoising operation conducts neighbor information aggregation as an independent preprocessing, and only two multilayer perceptrons (MLPs) are included as the backbone. For data augmentation, instead of introducing complex operations over graphs, we construct two augmented views of the same vertex by designing parameter unshared Siamese encoders and perturbing the node embeddings directly. Finally, as to the objective function, to further improve the clustering performance, a novel cross-view structural consistency objective function is designed to enhance the discriminative capability of the learned network. Extensive experimental results on seven benchmark datasets validate our proposed algorithm's effectiveness and superiority. Significantly, our algorithm outperforms the recent contrastive deep clustering competitors with at least seven times speedup on average. The code of SCGC is released at SCGC. Besides, we share a collection of deep graph clustering, including papers, codes, and datasets at ADGC.

The influence maximization algorithm for integrating attribute graph clustering and heterogeneous graph transformer

In social networks, maximizing influence is an important research direction. However, traditional influence maximization algorithms often overlook the attribute information of nodes and the heterogeneity of networks, leading to inefficiency and inaccuracy in the propagation process. To address this issue, this study first constructs a social network influence maximization propagation model, and then combines auto-encoder and graph convolutional autoencoder to extract social network user attributes. Finally, Transformer is used to learn the feature representation of social network nodes. Moreover, a heterogeneous graph neural network is introduced to combine with Transformer to further learn the feature representation of nodes, so as to design an influence maximization algorithm combining attributed graph clustering and heterogeneous graph Transformer. The results showed that the loss values of the fusion algorithm on different datasets were 0.619 and 0.17, respectively, proving its good fitting performance. The recall rate and F1 of this fusion algorithm were 92.5 % and 0.90, respectively, proving its high clustering accuracy. The influence maximization model based on fusion algorithm achieved active node coverage of 67 % and 48 % on two datasets, respectively, proving its good influence propagation effect. The above results demonstrate that the designed model can effectively spread influence. This helps to better understand and utilize the influence dissemination mechanisms in social networks, thereby promoting the development of social network research.

KNEG-CL: Unveiling data patterns using a k-nearest neighbor evolutionary graph for efficient clustering

KNEG-CL: Unveiling data patterns using a k-nearest neighbor evolutionary graph for efficient clustering

Dynamic graph attention-guided graph clustering with entropy minimization self-supervision

Dynamic graph attention-guided graph clustering with entropy minimization self-supervision

Attributed Graph Clustering Network with Adaptive Feature Fusion

Attributed Graph Clustering Network with Adaptive Feature Fusion

Deep attributed graph clustering with feature consistency contrastive and topology enhanced network

Deep attributed graph clustering has attracted considerable interest lately due to its capability to uncover meaningful latent knowledge from heterogeneous spaces, thereby improving our comprehension of real-world systems. However, ensuring the consistency of the clustering assignments generated from topological and attribute information remains a key issue, which is one of the reasons for the low performance of clustering. To tackle these issues, a novel deep clustering approach with Feature Consistency Contrastive and Topology Enhanced Network (FCC-TEN) is proposed, which consists of GAT and AE that can mine the topological and attributed information and achieve consistency contrastive learning to improve clustering performance. First, a Fusion Graph Convolutional Auto-encoder module is proposed to fuse the attribute information captured by each layer of the AE and enrich topological information for improving the feature extraction capability of AE. Then, using a Feature Consistency Contrastive module to uncover consistency information of the GAT and AE through contrastive learning at the feature and label level. Finally, clustering results are obtained directly by the clustering assignment obtained at the label level. Comprehensive testing on five improved datasets shows that our method provides advanced clustering performance. Moreover, visual analyses of the clustering results corroborate a gradual refinement of the clustering structure, proving the validity of our approach.

GCFExplainer: Global Counterfactual Explainer for Graph Neural Networks

Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to address this issue involves using counterfactual reasoning where the objective is to alter the GNN prediction by minimal changes in the input graph. Existing methods for counterfactual explanation of GNNs are limited to instance-specific local reasoning. This approach has two major limitations of not being able to offer global recourse policies and overloading human cognitive ability with too much information. In this work, we study the global explainability of GNNs through global counterfactual reasoning. Specifically, we want to find a small set of representative counterfactual graphs that explains all input graphs. Towards this goal, we propose GCF Explainer , a novel algorithm powered by vertex-reinforced random walks on an edit map of graphs with a greedy summary . Extensive experiments on real graph datasets show that the global explanation from GCF Explainer provides important high-level insights of the model behavior and achieves a 46.9% gain in recourse coverage, a 9.5% reduction in recourse cost compared to the state-of-the-art local counterfactual explainers. We also demonstrate that GCF Explainer generates explanations that are more consistent with input dataset characteristics, and is robust under adversarial attacks. In addition, K-GCF Explainer , which incorporates a graph clustering component into GCF Explainer , is introduced as a more competitive extension for datasets with a clustering structure, leading to superior performance in three out of four datasets in the experiments and better scalability.

Hierarchical Graph Neural Network: A Lightweight Image Matching Model with Enhanced Message Passing of Local and Global Information in Hierarchical Graph Neural Networks

Graph Neural Networks (GNNs) have gained popularity in image matching methods, proving useful for various computer vision tasks like Structure from Motion (SfM) and 3D reconstruction. A well-known example is SuperGlue. Lightweight variants, such as LightGlue, have been developed with a focus on stacking fewer GNN layers compared to SuperGlue. This paper proposes the h-GNN, a lightweight image matching model, with improvements in the two processing modules, the GNN and matching modules. After image features are detected and described as keypoint nodes of a base graph, the GNN module, which primarily aims at increasing the h-GNN’s depth, creates successive hierarchies of compressed-size graphs from the base graph through a clustering technique termed SC+PCA. SC+PCA combines Principal Component Analysis (PCA) with Spectral Clustering (SC) to enrich nodes with local and global information during graph clustering. A dual non-contrastive clustering loss is used to optimize graph clustering. Additionally, four message-passing mechanisms have been proposed to only update node representations within a graph cluster at the same hierarchical level or to update node representations across graph clusters at different hierarchical levels. The matching module performs iterative pairwise matching on the enriched node representations to obtain a scoring matrix. This matrix comprises scores indicating potential correct matches between the image keypoint nodes. The score matrix is refined with a ‘dustbin’ to further suppress unmatched features. There is a reprojection loss used to optimize keypoint match positions. The Sinkhorn algorithm generates a final partial assignment from the refined score matrix. Experimental results demonstrate the performance of the proposed h-GNN against competing state-of-the-art (SOTA) GNN-based methods on several image matching tasks under homography, estimation, indoor and outdoor camera pose estimation, and 3D reconstruction on multiple datasets. Experiments also demonstrate improved computational memory and runtime, approximately 38.1% and 26.14% lower than SuperGlue, and an average of about 6.8% and 7.1% lower than LightGlue. Future research will explore the effects of integrating more recent simplicial message-passing mechanisms, which concurrently update both node and edge representations, into our proposed model.

Towards attributed graph clustering using enhanced graph and reconstructed graph structure

Attributed graph clustering, leveraging both structural and attribute information, is crucial in various real-world applications. However, current approaches face challenges stemming from the sparsity of graphs and sensitivity to noise in Graph Convolutional Networks (GCNs). Moreover, GCN-based methods are often designed based on the assumption of homophilic graph and ignore heterophilic graph. To address these, we propose a graph clustering method that consists of four phases: graph enhance, graph reconstruction, graph refine, and dual-guidance supervisor module. An enhanced graph module is defined by an auxiliary graph to consider distant relationships in the topology structure to alleviate the limitations of sparse graphs. The graph reconstruction phase includes the creation and integration of homophily and heterophily graphs to achieve graph-agnostic. In graph refine, the auxiliary graph is iteratively improved to enhance the generalization of the representation. In this phase, a subspace clustering module is applied to convert attribute-based embeddings into relationship-based representations. Finally, the extracted graphs are fed to a dual-guidance supervisor module to obtain the final clustering result. Experimental validation on several benchmark datasets demonstrates the efficiency of our model. Meanwhile, the findings offer significant advancements in attributed graph clustering, promising improved applicability in various domains.

Community detection using Jaya optimization algorithm based on deep learning methods and KNN graph-based clustering

Purpose In this paper, community identification has been considered as the most critical task of social network analysis. The purpose of this paper is to organize the nodes of a given network graph into distinct clusters or known communities. These clusters will therefore form the different communities available within the social network graph. Design/methodology/approach To date, numerous methods have been developed to detect communities in social networks through graph clustering techniques. The k-means algorithm stands out as one of the most well-known graph clustering algorithms, celebrated for its straightforward implementation and rapid processing. However, it has a serious drawback because it is insensitive to initial conditions and always settles on local optima rather than finding the global optimum. More recently, clustering algorithms that use a reciprocal KNN (k-nearest neighbors) graph have been used for data clustering. It skillfully overcomes many major shortcomings of k-means algorithms, especially about the selection of the initial centers of clusters. However, it does face its own challenge: sensitivity to the choice of the neighborhood size parameter k, which is crucial for selecting the nearest neighbors during the clustering process. In this design, the Jaya optimization method is used to select the K parameter in the KNN method. Findings The experiment on real-world network data results show that the proposed approach significantly improves the accuracy of methods in community detection in social networks. On the other hand, it seems to offer some potential for discovering a more refined hierarchy in social networks and thus becomes a useful tool in the analysis of social networks. Originality/value This paper introduces an enhancement to the KNN graph-based clustering method by proposing a local average vector method for selecting the optimal neighborhood size parameter k. Furthermore, it presents an improved Jaya algorithm with KNN graph-based clustering for more effective community detection in social network graphs.

Auto-weighted Graph Reconstruction for efficient ensemble clustering

Ensemble clustering (EC) is an important field in data clustering, which aims to combine multiple base clustering results of a given dataset into a single consensus result. Previous studies have shown that one of the effective methods is to convert the base clustering results into a graph partitioning problem. Existing spectral ensemble clustering methods typically obtain a spectral embedding matrix for the graph and then perform k-means, separating the graph construction and clustering tasks into two distinct stages. To address these challenges, we propose a novel and efficient ensemble clustering method named Auto-weighted Graph Reconstruction for efficient ensemble clustering. This method integrates weighted base clusters, consensus distance graph construction, and clustering into a unified framework. A consensus distance graph is constructed, from which a stable and discrete label matrix is derived using spectral clustering. An effective optimization algorithm is then employed to address the resulting problem. Finally, the experimental results on eight real-world datasets verify the effectiveness and superiority of the proposed method.