Bipartite clustering Abstract—The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets. More recently, Li et al. Apr 1, 2025 · Given a set of base clustering results, conventional bipartite graph-based ensemble clustering methods typically require computing a sample-cluster similarity matrix from each base clustering result. They construct bipartite graphs in the raw feature space, inducing poor robustness to noisy features. It must be "dot", "max", or "min". We focus on the adjacency-based Apr 1, 2025 · Abstract Given a set of base clustering results, conventional bipartite graph-based ensemble clustering methods typically require computing a sample-cluster similarity matrix from each base clustering result. Zhou, "Bipartite Graph-based Projected Clustering with Local Region Guidance for Hyperspectral Imagery," in IEEE Transactions on Abstract We consider spectral clustering algorithms for community detection under a general bi-partite stochastic block model (SBM). Apr 15, 2025 · To handle these drawbacks, we propose an unified framework that allows for jointly learning consensus anchor matrix and tensorized bipartite graph, as well as integrating a fast spectral embedding technique. Returns: clusteringdictionary A dictionary keyed by node with the clustering coefficient value. It must be “dot”, “max”, or “min”. The bipartie clustering coefficient is a measure of local density of connections defined as [1]: Feb 1, 2020 · The key point of utilizing the multiple bipartite graphs to deal with the multi-view co-clustering task is to reasonably integrate these bipartite graphs and obtain an optimal consensus one. The nodes should be either the entire graph (the default) or one of the bipartite sets. Instead of employing complex spectral analysis techniques, the proposed method adopts a bipartite graph factorization framework. ABSTRACT A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely used to model real-world data, such as user-item purchase records, author-article publications, and bio-logical interactions between drugs and proteins. clustering ¶ clustering(G, nodes=None, mode='dot') ¶ Compute a bipartite clustering coefficient for nodes. Hereto, the quality of both anchors and bipartite graphs plays a vital role in multi-view clustering. Then we compare the two definitions in a special graph, and the results show that the Aug 24, 2024 · Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, such as customer-product purchase networks and author-paper authorship graphs. networkx. In this paper, we present a nonconvex tensor method with bipartite graph regularization for multiview subspace clustering. BiTSC novelly implements a formulation that encodes gene orthology as a bipartite network and gene expression data as node covariates. At present, the subspace clustering methods based on the abstract graph have better performance and improve the clustering results. It must be “dot”, “max”, or “min” Returns: clusteringfloat The average bipartite Oct 10, 2022 · In this paper, we propose bipartite graph-based discriminative feature learning for multi-view clustering, which combines bipartite graph learning and discriminative feature learning to a unified framework. In this paper, with the similar consideration of standard clustering coefficient in binary networks, a definition of the clustering coefficient for bipartite networks based on the fraction of squares is proposed. suboptimal clustering performance. Returns ------- clustering : dictionary A dictionary keyed by node with the clustering coefficient value. These matrices are then either concatenated or averaged to form a bipartite weight matrix, which is used to create a bipartite graph. When generating these random graphs, we initially generate k disjoint, fully nested components, followed by executing two transformations within each component. Partitioning the target node set in such graphs into k disjoint clusters (referred to as k-ABGC) finds widespread use in various domains enforce the view-specific bipartite graph learning and the view-consensus bipartite graph learning. Mar 26, 2024 · A bipartite graph co-clustering approach to ontology mapping. A modern spectral clustering algorithm consists of three steps: (1) regularization of an appropriate adjacency or Laplacian matrix (2) a form of spectral truncation and (3) a k-means type algorithm in the reduced spectral domain. The clustering quality Amid them, BGCC has been extensively investigated in the literature [2, 9, 10, 29, 31, 66] for clustering non-attributed bipartite graphs, whose basic idea is to simultaneously group nodes in U and V merely based on their interactions in G, instead of clustering them severally. In this paper, we present the Attributed Bipartite Co-clustering (ABC) problem which unifies two main con. b) Inflexible anchor selection strategies. Apr 1, 2025 · To overcome this challenge, we propose a Scalable Unpaired Multi-view Clustering with Bipartite Graph Matching (SUMC-BGM). In this paper, we present the Attributed Bipartite Co-clustering (ABC) problem which unifies two main con Jan 1, 2017 · Request PDF | On Jan 1, 2017, Christian Freund published Bipartite Clustering in Bank-Firm Networks | Find, read and cite all the research you need on ResearchGate clustering ¶ clustering(G, nodes=None, mode='dot') ¶ Compute a bipartite clustering coefficient for nodes. Jul 1, 2024 · On the basic framework of multi-view spectral clustering, we propose a new clustering algorithm–Generalized Latent Multi-View Clustering with Tensorized Bipartite Graph (GLMC-TBG). Many real-world networks display a natural bipartite structure. Jan 1, 2025 · Multi-view subspace clustering optimizes and integrates the graph structure information of each view. However, most Download scientific diagram | Examples of bipartite clustering coefficients, and interpretations. Based on this, we constrain the anchor with discriminative cluster structure and fair view allocation, and then obtain a better bipartite graph for fast clustering. However, the existing bipartite graph clustering paradigm pays little attention to the adverse impact of noisy features on learning process. Jan 15, 2025 · Bipartite graph (BiG) has been proven to be efficient in handling massive multiview data for clustering. -Bipartite Graph Clustering ( -BGC) is to partition the target vertex set in a bipartite graph into disjoint clusters. In BSGP, the key is to find the minimal normalized cuts (Ncuts) of bipartite graph. We propose an algorithm called Bipartite Assisted Spectral-clustering for Identifying Communities (BASIC) under DCBM and its bipartite modification BiDCBM. Different from the traditional graph-based methods, co-clustering can utilize the bipartite graph to extract the We consider spectral clustering algorithms for community detection under a general bipartite stochastic block model (SBM). Although many variants are proposed by vari-ous strategies, a common design is to construct the bipartite graph directly from the input data, i. NetworkX does not have a custom bipartite graph class but the Graph () or DiGraph () classes can be used to represent bipartite graphs. , clusters) in the bipartite Our spectral cluster-ing algorithm creates a bipartite graph and is based on the “minimizing-disagreement” idea. In synthetic mixtures with known ground truth, the filter achieves high F-scores and sharpens inference of Jun 1, 2022 · Finding a set of co-clusters in a bipartite network is a fundamental and important problem. Aug 27, 2001 · Many data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender system. Apr 1, 2025 · Then, a bipartite graph with a consistent structure is constructed by establishing connections between anchors and samples, where the ultimate clustering results are directly obtained from the bipartite graph. Co-clustering in a bipartite graph can be naturally formulated as a graph- partitioning problem, which aims at getting the vertex partition with minimum cut (Dhillon 2001; and Zha et al. Jan 1, 2025 · To solve the limitations of the above problems, we propose a multi-view clustering algorithm with adaptive anchor and bipartite graph learning (ABMVC), which directly outputs c-connected bipartite graphs and cluster labels without additional post-processing. It must be “dot”, “max”, or “min” Returns: clusteringfloat The average bipartite Apr 15, 2025 · We name our method as Unified and Efficient Multi-View Clustering with Tensorized Bipartite Graph (UEMC-TBG). May 1, 2021 · Bipartite networks form an important class of complex networks because they reveal the heterogeneity of nodes in a network. To further facilitate this part of research, apart from Mar 14, 2024 · Multi-view graph clustering can divide similar objects into the same category through learning the relationship among samples. Left: a case in which cc• (u, v) = 2 6 from publication: Basic Notions for the Analysis of Large Abstract Co-clustering methods have been widely applied to document clustering and gene expression analysis. CCBIE facilitates automatic and dynamic soft clustering of items in a top-down manner, and capturing macro-preference information of users through clusters. In this paper, we have introduced the novel idea of mod-eling a document collection as a bipartite graph using which we proposed a spectral algorithm for co-clustering words and documents. Aug 15, 2025 · Learning consensus and complementary bipartite graph through orthogonal transformation for multi-view subspace clustering In this article, we focus on utilizing the idea of co-clustering algorithms to address the subspace clustering problem. In this paper, we propose a bipartite graph-based projected clustering (BGPC) method with local region guidance for HSI data. Aug 1, 2025 · Our study addresses this gap by introducing a novel predictive model, DTI-BGCGCN, which integrates a bipartite drug- target attribute graph with a cluster graph convolutional network to predict drug-target interactions in both modern medicine and traditional Chinese medicine, effectively enhancing the accuracy of DTI predictions. MCHBG learns a structured fusion bipartite graph under the Laplacian rank constraint, which directly indicates the clusters of data. In contrast to previous clustering methods, our approach does not introduce any additional parameters and entirely relies on self-weighting for the fusion of view-specific graphs. Parameters: Ggraph A bipartite graph nodeslist or iterable (optional) Compute bipartite clustering for these nodes. We introduce a statistical filter that benchmarks node-level bipartite clustering against degree-preserving randomizations to classify nodes as geometric (signal) or random-like (noise). Specifically, first learn similarity graph matrices of multiple views and then fuse them into a unified superior graph matrix. May 1, 2025 · We propose a multi-view subspace clustering based on hierarchical bipartite graph to effectively handle large-scale data clustering task. Most Finding a set of co-clusters in a bipartite network is a fundamental and important problem. While we find only moderate evidence of structural changes in the network over the sample period, bootstrapping simulations yield very strong evidence for nonrandom linking behavior between banks and firms. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature Oct 12, 2024 · In this paper, we propose a Clustering Constraints induced BIpartite graph Embedding (CCBIE) as an integrated solution to both problems. Despite BGC has achieved promising scalability, most variants still suffer from the following concerns: a) Susceptibility to noisy features. We name our method as Unified and Efficient Multi-View Clustering with Tensorized Bipartite Graph (UEMC-TBG). Then we compare the two definitions in a special graph, and the results show that the Dec 28, 2024 · Conventional bipartite graph-based cluster ensembles usually compute a weight matrix from a base clustering result to represent the similarities between the samples and clusters. Berkeley, CA 94720 Compute the bipartite clustering of G. Departing from conventional… Some recent research endeavors are devoted to reducing the algorithm complexity. Sci. In this paper, we present the Attributed Bipartite Co-clustering (ABC) problem which unies two main concepts: (i) bipartite modularity optimization, and (ii) attribute cohesiveness. It is necessary and important to study the bipartite networks by using the bipartite structure of the data. SIGMOD 2024 paper titled "Efficient High-Quality Clustering for Large Bipartite Graphs" - HKBU-LAGAS/HOPE Results: Here we develop the bipartite tight spectral clustering (BiTSC) algorithm, which identifies gene co-clusters in two species based on gene orthology information and gene expression data. Specifically, UEMC-TBG captures the high-order correlations of multiple bipartite graphs with consensus anchors. Dec 1, 2024 · Regarding the above issues, this paper proposes a novel multi-view clustering method via dynamic unified bipartite graph learning. Parameters: Ggraph a bipartite graph nodeslist or iterable, optional A container of nodes to use in computing the average. The default is all nodes in G. Jul 1, 2020 · Graph datasets are frequently constructed by a projection of a bipartite graph, where two nodes are connected in the projection if they share a common neighbor in the bipartite graph; for example, a coauthorship graph is a projection of an author-publication bipartite graph. A modern spectral clustering algorithm consists of three steps: (1) regularization of an appropriate adjacency or Laplacian matrix (2) a form of spectral truncation and (3) a kmeans type algorithm in the reduced spectral Mar 1, 2023 · Multiview clustering, which partitions data into different groups, has attracted wide attention. State College, PA 16802 Chris Ding Horst Simon NERSC Division Berkeley National Lab. The main advantage of bipartite graph is to select/sample a relative small propor-tion of representative landmarks and explore the relation-ship between anchors and original samples. These weight matrices are concatenated or averaged as a bipartite weight matrix with which a bipartite graph is created, and then graph-based partition techniques are used to partition the samples in the bipartite Apr 1, 2025 · Then, a bipartite graph with a consistent structure is constructed by establishing connections between anchors and samples, where the ultimate clustering results are directly obtained from the bipartite graph. To overcome this disadvantage, we propose a Normalized Cut Co-Clustering (NC 3) model, which assigns clusters for samples and anchors by alternatively updating the discrete label matrices. Most current methods learn pairwise similarities between data points for each view To address these challenges, this paper presents an efficient multi-view clustering approach via Essential Tensorized Bipartite Graph Learning (ETBGL). We propose a cluster-structured graph learning model, thus obtaining a c -connected (c is the cluster number) bipartite graph and also getting discrete labels straightforward. Then, with the devised sample alignment and anchor integration strategy, these bipartite graphs are fused to learn a joint bipartite graph with explicit cluster membership structure. Oct 28, 2022 · However, most existing bipartite graph-based multi-view clustering methods have the following disadvantages: 1) the clustering performance heavily depends on the predefined bipartite graph; 2) they fail to explore the complementary information embedded in multiple bipartite graphs and spatial low-rank structure hidden in each bipartite graph. Most current methods learn pairwise similarities between data points for each view independently, which is widely used in single view Bipartite graph has been widely regarded as an effective strategy to deal with large-scale datasets in multi-view spec-tral clustering [7,10,11,28,29,35]. To avoid the post-processing via k-means during the bipartite graph partitioning, the constrained Laplacian rank (CLR) is often utilized for constraining the number of connected components (i. Jiang, Z. Apr 15, 2025 · We name our method as Unified and Efficient Multi-View Clustering with Tensorized Bipartite Graph (UEMC-TBG). Mar 26, 2025 · In this section, we analyze the bipartite network described so far via the proposed biclustering approach to obtain a joint clustering of patients and clinical conditions. Robins and Alexander [1] defined bipartite clustering coefficient as four times the number of four cycles C_4 divided by the number of three paths L_3 in a bipartite graph: Feb 4, 2025 · To address these challenges, we propose a superpixel-based bipartite graph clustering (SBGC) enriched with spatial information for hyperspectral and LiDAR data models. The bipartie clustering coefficient is a measure of local density of connections defined as [1]: Y. Bipartite Graph Partitioning and Data Clustering Hongyuan Zha Xiaofeng He Dept. of Comp. Dec 1, 2023 · Multi-view bipartite graph clustering methods select a few representative anchors and then establish a connection with original samples to generate the bipartite graphs for clustering, which maintains impressive performance while reducing time and space complexities. Although the existing clustering algorithms have achieved excellent results, there are still four deficiencies: 1) Expensive time overhead, with most algorithms Mar 10, 2025 · View a PDF of the paper titled BASIC: Bipartite Assisted Spectral-clustering for Identifying Communities in Large-scale Networks, by Tianchen Gao and 3 other authors Multi-view spectral clustering has become appealing due to its good performance in capturing the correlations among all views. modestring The pairwise bipartite clustering method to be used in the computation. Second, under the However, the computation of high-order proximity matrices and clustering based on them are expensive. Dec 28, 2023 · A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely used to model real-world data, such as user-item purchase records, author-article publications, and biological interactions between drugs and proteins. Furthermore, they mostly regard the bipartite graph learning and its spectral partitioning as two separate phases, yet cannot directly obtain the clustering result by learning a unified bipartite graph with discrete cluster structure. where X is a bipartite set of G. Accordingly, a bipartite graph based spectral clustering method is proposed to achieve efficient clustering with high accuracy. modestring The pairwise bipartite clustering method. Compute bipartite clustering for these nodes. The partition is constructed Dec 1, 2024 · The graph-based multi-view clustering method has gained considerable attention in recent years. Model fitting is reformulated as a bipartite graph partition behavior. Lusi Li and Haibo He, Fellow, IEEE Abstract—For graph-based multi-view clustering, a critical issue is to capture consensus cluster structures via a two-stage learning scheme. The bipartie clustering coefficient is a measure of local density of connections defined as [1]: Abstract We consider spectral clustering algorithms for community detection under a general bi-partite stochastic block model (SBM). Current most methods learn graph structure using one-order bipartite PAGG uses the prior anchor labels (PAL) to directly supervise anchor graph, and then backpropagate to the consensus anchor. Similar to the idea of an anchor graph, firstly, the bisecting k-means method is used instead of traditional method to generate a hierarchical anchor points set. These weight matrices are concatenated or averaged as a bipartite weight matrix with which a bipartite graph is created, and then graph-based partition techniques are used to partition the samples in the bipartite May 1, 2025 · We propose a multi-view subspace clustering based on hierarchical bipartite graph to effectively handle large-scale data clustering task. In recent years, co-clustering methods have been developed greatly with many important applications, such as document clustering and gene expression analysis. For unweighted graphs, the clustering of a node u is the fraction of possible triangles through that node that exist, Specifically, view-specific bipartite graphs with diversified anchors are constructed to adapt to the characteristics of unaligned multi-view data. Bipartite graph clustering (BGC) has emerged as a fast-growing research in the clustering community. clustering ¶ clustering(G, nodes=None, mode='dot') ¶ Compute a bipartite clustering coefficient for nodes. Sep 3, 2020 · For graph-based multi-view clustering, a critical issue is to capture consensus cluster structures via a two-stage learning scheme. SUMC-BGM has devised a novel bipartite graph matching framework to learn a consistent structure bipartite graph for clustering from large-scale unpaired data. During the iterations, undesirable high-frequency noise is gradually filtered out to achieve the clustering-friendly smooth representation. To improve clustering efficiency, instead of all sample-based graph learning, the bipartite graph learning method can achieve efficient clustering by establishing the graph between data points and a few anchors, so it becomes an important research topic. However, how to regulate the structural information of view-specific anchors and view-shared BiG is still open and needs to be further studied. k-Bipartite Graph Clustering (k-BGC) is to partition the target vertex set in a bipartite graph into k disjoint clusters. Sep 7, 2024 · Finally, we propose a new solver to handle these problems, enabling the structured bipartite graphs to directly indicate clustering results. This type of bipartite graph-based multi-view clustering algorithm is highly efficient. & Eng. In order to detect community structures in bipartite networks, two different edge Oct 1, 2023 · In contrast to traditional spectral clustering algorithms that obtain clustering results in a two-step process, FSBGL obtains clustering results directly from the concatenated components of the optimized similarity matrix while learning the optimized bipartite graph. As reviewed in Section 5, existing solutions towards -ABGC primarily rely on (BGCC), bipartite graph co-clustering attributed (AGC), and (ANE) graph clustering attributed network embedding techniques. They Jan 1, 2025 · To solve the limitations of the above problems, we propose a multi-view clustering algorithm with adaptive anchor and bipartite graph learning (ABMVC), which directly outputs c -connected bipartite graphs and cluster labels without additional post-processing. The clustering quality is Bipartite spectral graph partitioning (BSGP) method as a co-clustering method, has been widely used in document clustering, which simultaneously clusters documents and words by making full use of the duality between documents and words. Despite their effectiveness in large-scale applications, few of them focus on cross-view anchor misalignment (CAM) problem. , only consider the unidirectional “encoding” process. Jan 1, 2021 · Bipartite spectral graph partition (BSGP) is a school of the most well-known algorithms designed for the bipartite graph partition problem. However, the convolutional BSGP algorithms usually need After modeling two ontologies as a bipartite graph, we apply bipartite graph co-clustering technique to establish mappings between two ontologies. cluster. Apr 15, 2025 · Therefore, we propose an unified framework that enables the joint learning of consensus anchors and tensorized bipartite graph, and integrates a fast spectral embedding, namely Unified and Efficient Multi-View Clustering with Tensorized Bipartite Graph (UEMC-TBG). Cai and Y. With increasing data, bipartite graph-based multiview clustering has become an important topic since it can achieve efficient clustering by establishing relationship between data points and anchor points instead of all samples. We develop an effective and efficient optimization algorithm for our method, and provide elegant theoretical results about the convergence. where N(N(u)) are the second order neighbors of u in G excluding u, and c_{uv} is the pairwise clustering coefficient between nodes u and v. org Oct 1, 2025 · In this section, we propose an innovative clustering model that dynamically learns bipartite graphs, called Clustering with Dynamic Bipartite Graph Learning (DBGL). To solve the above problem, inspired by the anchor bipartite graph, we present a novel high-order bipartite graph proximity matrix and a fast method to compute it. The factor matrices are learned with bipartite graph partitioning, which exploits explicit cluster structure of the data and is more geared towards clustering application. This eliminates the effect of performing these two steps separately in SC. Oct 21, 2024 · To gain a deeper understanding of the clustering algorithm’s stability, we create random bipartite graphs with a known ground truth cluster structure. Different from traditional anchor-based co-clustering methods, our model solves the original discrete normalized cut problem on the bipartite graph directly. Then Abstract A critical problem in cluster ensemble re-search is how to combine multiple cluster-ings to yield a nal superior clustering re-sult. However, “encoding-decoding” mechanism clustering # clustering(G, nodes=None, weight=None) [source] # Compute the clustering coefficient for nodes. bipartite. In graph based co-clustering methods, a bipartite graph is constructed to depict the relation between features and samples. Specifically, the bipartite graph learning is proposed via multi-view subspace representation with manifold regularization terms. To take full advantage of spatial information, HSI denoising to alleviate noise interference and anchor initialization to construct bipartite graph are conducted within each generated superpixel. See full list on jmlr. The mode selects the function for c_{uv} which can be: A bipartite graph. Meanwhile, a novel bipartite graph matching framework is devel-oped to align unpaired bipartite graphs, creating a consistent bipartite graph from unpaired multi-view data. We also compare our algorithm with one state-of-the-art bipartite network clustering algorithm and one highly efficient hierarchical network clustering method. The resulting graph models Unsupervised bipartite graph learning has been a hot topic in multi-view clustering, to tackle the restricted scalability issue of traditional full graph clustering in large-scale applications. Penn State Univ. Here we propose a modification of the clustering coefficient given by the fraction of cycles with size four in bipartite networks. Although many variants are proposed by various strategies, a common design is to construct the bipartite graph directly from the input data, i. In this work, a novel constrained agglomerative clustering method applicable to unipartite and bipartite networks has been proposed. We would like to show you a description here but the site won’t allow us. Abstract—Unsupervised multi-view bipartite graph clustering (MVBGC) is a fast-growing research, due to promising scalability in large-scale tasks. This is useful if you have a bipartite graph and you want to estimate the amount of memory you would need to calculate the projections themselves. Jan 1, 2025 · For the third challenge, our graph fusion mechanism selectively integrates high-order bipartite graphs, and implicitly weights the selected bipartite graphs to mitigate the impact of low-quality bipartite graphs. Real bipartite networks combine degree-constrained random mixing with structured, locality-like rules. Jun 1, 2022 · Finding a set of co-clusters in a bipartite network is a fundamental and important problem. First, a bipartite graph is constructed to capture the structure of samples, providing a more suitable representation. [22] proposed a scalable multi-view We propose an algorithm called Bipartite Assisted Spectral-clustering for Identifying Communities (BASIC) under DCBM and its bipartite modification BiDCBM. These methods make use of the duality between features and samples such that the co-occurring structure of sample and feature clusters can be extracted. However, most extant clustering methods focus only on unipartite networks. The motivation for this research stems from the authors' perception that state-of-the-art research on bipartite graphs was missing a key element in network analysis: a strong null model. Unsupervised multi-view bipartite graph clustering (MVBGC) is a fast-growing research, due to promising scalability in large-scale tasks. However, on one hand, many existing methods usually require a quadratic or cubic complexity for graph construction or eigenvalue decomposition of Laplacian matrix; on the other hand, they are inefficient and unbearable burden to be applied to large scale data sets In this paper, we propose a novel model fitting method based on co-clustering on bipartite graphs (CBG) to estimate multiple model instances in data contaminated with outliers and noise. Bipartite graph is also widely used to speed up spectral clustering [22]–[24]. Jun 26, 2023 · In order to largely improve the quality of anchors, PAGG predefines prior anchor labels to constrain the anchors with discriminative cluster structure and fair view allocation, such that a better bipartite graph can be obtained for fast clustering. Feb 3, 2023 · To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. This includes clustering bipartite graphs across a range of parameters, detecting motif-rich clusters in an email network and a food web, and forming clusters of retail products in a product review hypergraph, that are highly correlated with known product categories. Amid them, BGCC has been extensively investigated in the literature [2, 8, 9, 28, 29, 63] for clustering non-attributed bipartite graphs, whose basic idea is to simultaneously group nodes in U Apr 1, 2025 · Abstract Given a set of base clustering results, conventional bipartite graph-based ensemble clustering methods typically require computing a sample-cluster similarity matrix from each base clustering result. Departing from conventional… May 20, 2024 · Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, such as customer-product purchase networks and author-paper authorship graphs. Nov 7, 2024 · Aiming at these two problems, we propose a novel auto-weighted multi-view clustering method based on the hierarchical bipartite graph to effectively address these two limitations. Apr 1, 2025 · In this paper, we propose a novel multi-view bipartite graph clustering, namely scalable sparse bipartite graph factorization for multi-view clustering. We introduce a new reduction method that con-structs a bipartite graph from a given cluster ensemble. [13] proposed a simple and effective multi-view attribute graph clustering algorithm, obtained the smooth representation of data through graph filter, and designed a new strategy to select anchor points. Oct 1, 2023 · In contrast to traditional spectral clustering algorithms that obtain clustering results in a two-step process, FSBGL obtains clustering results directly from the concatenated components of the optimized similarity matrix while learning the optimized bipartite graph. Jan 25, 2025 · In this paper, we present a correntropy-based bipartite graph factorization method for clustering, which reduces the dependency of clustering efficiency on the dimensionality of the original data by constructing a bipartite graph. The eficacy, eficiency, and superiority of our FUMC are corroborated through extensive evalua-tions on numerous benchmark datasets with shal-low and deep SOTA methods. Analyzing the structure of the projected graph is common, but we do not have a good understanding of the consequences of Calculates the number of vertices and edges in the bipartite projections of this graph according to the specified vertex types. In the context of subspace clustering, we can regard the data points as one set and the potential subspaces or anchor points as the other set, then leverage the bipartite graph to model the membership relationships between data points and subspaces. We show a simple artifically generated problem to illustrate when we expect it to perform well and then apply it to a web page clustering problem. We handily integrate the adjacency matrices of both the primary and bipartite networks, then conduct an eigenvalue decomposition toward the aggregated matrix, and apply the SCORE May 2, 2018 · To evaluate the effectiveness of our proposed bottom-up hierarchical clustering algorithm, we carry out experiments on ten bipartite ecological networks. First, a dynamic filter was designed to address the impact of noise and outliers on clustering performance. In this paper, we propose a new data clustering method based on partitioning the underlying bipartite graph. It is also a fundamental mathematical model widely used in the tasks of co-clustering and fast spectral clustering. 2001). For example, some off-the-shelf projec-tion and sampling methods are applied for spectral clustering [21]. Jun 14, 2017 · In particular we analyze the bipartite clustering between banks and firms with several different statistics. In this paper, we provide a uni ed treatment of the community detection, or clustering, in the bipartite setting with a focus on deriving fundamental theoretical limits of the problem. Specifically, ETBGL utilizes the low-rank tensor Schatten p -norm to capture inter-view similarity, effectively capturing high-order correlation information embedded in multiple views. e. To get rid of the ad-hoc functions used to calculate similarity, several scalable subspace clustering methods are proposed Jul 1, 2023 · Firstly, compared to traditional subspace clustering algorithms such as LRR and SSC that construct graph directly with the obtained coefficient matrix, our method FSBLSC and another method LAPIN construct bipartite graph between sample and dictionary, which better exploit the clustering information and therefore obtain better results on each Dec 1, 2008 · Many real-world networks display natural bipartite structure, where the basic cycle is a square. Zhang, G. We focus on the adjacency-based Jan 30, 2022 · To further eliminate the impact of transformation of the bipartite graph, the bipartite graph is directly used for clustering such that more information can be retained. Partitioning the target node set in such graphs into k disjoint clusters (referred to as k-ABGC) finds widespread use in various domains Dec 28, 2024 · Conventional bipartite graph-based cluster ensembles usually compute a weight matrix from a base clustering result to represent the similarities between the samples and clusters. It consists of two steps: 1) graph construction and 2) singular value decomposition on the bipartite graph to compute a continuous cluster assignment matrix Dec 1, 2024 · Lin et al. To the best of our knowledge, this is therst work to nd co-clusters while considering the attribute cohesiveness Jul 31, 2024 · To tackle these issues, we propose a correntropy-based bipartite graph factorization model for clustering (CBGFC). Leveraging advanced graph partitioning techniques, we solve this problem by reduc-ing it to a graph partitioning problem. algorithms. May 18, 2023 · In this paper, we investigate the scalability, stableness and integration in large-scale graph clustering. mode : string The pairwise bipartite clustering method to be used in the computation. We launched an investigation into null models for bipartite graphs, speci cally for the import-export weighted, directed bipartite graph of world trade. In Proceedings of the Workshop on Semantic Web Technologies for Searching and Retrieving Scientific Data. Jan 30, 2022 · To further eliminate the impact of transformation of the bipartite graph, the bipartite graph is directly used for clustering such that more information can be retained. However, you have to keep track of which set each node belongs to, and make sure that there is no edge between nodes of the same set. Applying the two clustering coefficients directly to a two-mode network is senseless as two-mode networks are bipartite and, thus, the contacts of a node cannot be connected to each other by construction and no triangles can exist (Borgatti and Everett, 1997). However, due to its large time complexity, it is limit… Oct 1, 2025 · However, despite integrating the cluster indicator matrix and the clustering process into a unified framework, it still generates a single-scale bipartite graph for each view, which limits the accurate capture of the original data structure and leads to suboptimal clustering results. yiudqctc dcfi vifhb flsfz ithl dsxzg prt nufbwi nlnolt xnbbg mbga plsw dcdlk fjfcs yheumns