Patent data generally includes information from different perspectives or different types, and its heterogeneous attributes can be greatly beneficial to data clustering analysis. However, the existing patent analysis method always focus on the patent text cues, and such a strategy merely depends on the feature information to capture the data characteristics, failing to multi-type informative patent representation. Therefore, in this paper, to model the underlying structure/relationships of patent data, we employ the knowledge graph to depict the heterogeneous attributes of patent, and propose a novel Knowledge Graph-based Patent Clustering (KGPC) method, where the relationship reconstruction in knowledge graph as well as clustering-oriented representation refinement for patent clustering are jointly considered. With this model, there are three components, i.e., entity representation refinement, relationship reconstruction and self-supervised entity clustering. Given a patent knowledge graph as input, the entity representation refinement can be mutually boosted by the relationship reconstruction and self-supervised clustering objective, thereby leading to a balanced clustering-oriented output. Extensive experiments on several real-world patent knowledge graph datasets validate the effectiveness of KGPC while compared with the state-of-the-art.

Due to the emergency of multi-view graph data, considerable attention is focused on the multi-view graph clustering. Although great efforts have been made in developing the multi-view graph clustering methods, most of them implicitly follow the homophily assumption, where the connected nodes with edges tend to be in the same category. As a matter of fact, such an ideal assumption is hard to be satisfied in the real-world graph data, and there are some heterogeneous edges connecting dissimilar nodes in graph. How to well consider the homophily and refine the noisy/heterogeneous edges in multi-view graph clustering still remains an under-explored challenge. Therefore, in this paper, we propose a Homophily-enhanced Adaptive Graph Refinement for Multi-view Graph Clustering (HomoMGC) method, where an adaptive graph refinement strategy is seamlessly designed. Specifically, a feature-oriented graph is constructed based on the shared feature, and an integrated graph is computed by averagely fusing all the input adjacent graphs. Then, the feature-oriented graph and integrated graph are stacked into a graph tensor with a low-rank tensor constraint, where a refined affinity probability matrix can be adaptively recovered from the integrated graph by considering multiple graph information as well as the semantics features. Extensive experiments on several benchmark datasets demonstrate the superiority of HomoMGC compared with the state-of-the-art graph clustering methods. For the code reproducibility, the source code of HomoMGC is public available at https://github.com/ManshengChen/Code-for-HomoMGc-master.

In next basket recommendation, baskets are usually formed through a large number of user interactions with items in the early stage. In general, the existing methods for next basket recommendation primarily focus on historical purchase behavior of users, assuming that user purchase interests are static, and overlook the dynamic and diverse changes in user purchase interests. In order to fully capture dynamic user interests and provide users with more diverse recommendations, we propose our method, Dynamic Heterogeneous Graph Prompt (DHGP), for next basket recommendation. By constructing a dynamic heterogeneous graph, we can adequately consider the influence of various interactive behaviors on the user's baskets at different times. Furthermore, we introduce a periodic dynamic heterogeneous prompt strategy to capture the interest directions between baskets from different users and provide users with more diverse interest directions. Extensive experimental validation on six real world datasets demonstrates that our method shows strong applicability across datasets under various conditions and outperforms several state-of-the-art recommendation methods. To the best of our knowledge, DHGP is the first next basket recommendation method that effectively combines dynamic and heterogeneous information. The implementation code is accessible at https://github.com/AllminerLab.

Most existing large-scale multiview clustering algorithms attempt to capture data distribution in multiple views by selecting view-wise anchor representations beforehand with$k$-means, or by direct matrix factorization on the original observations. Despite impressive performance, few of them have paid attention to the semantic correlations between anchor bases and cluster centroids, or even the underlying relations between clusters and data samples. In view of this, we propose aConceptFactorization basedMultiviewClustering for Large-scale Data (CFMC) method with nearly linear complexity. The anchor bases learning, coefficient expression with clear semantic cues and partitioning are integrated together in this unified model. Meanwhile, explicit connections among multiview data, anchor bases and clusters are modeled via coefficient representations with semantic meanings. A four-step alternate minimizing algorithm is designed to handle the optimization problem, which is proved to have linear time complexityw.r.t.the sample size. Extensive experiments conducted on several challenging large-scale datasets confirm the superiority of the method compared with the state-of-the-art methods.

Abstract Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems. To address these issues, we propose a new contrastive learning-based graph collaborative filtering method to learn more robust representations. The proposed method is called signal contrastive enhanced graph collaborative filtering (SC-GCF), which conducts contrastive learning on graph signals. It has been proved that graph neural networks correspond to low-pass filters on the graph signals from the graph convolution perspective. Different from the previous contrastive learning-based methods, we first pay attention to the diversity of graph signals to directly optimize the informativeness of the graph signals. We introduce a hypergraph module to strengthen the representation learning ability of graph neural networks. The hypergraph learning module utilizes a learnable hypergraph structure to model the latent global dependency relations that graph neural networks cannot depict. Experiments are conducted on four public datasets, and the results show significant improvements compared with the state-of-the-art methods, which confirms the importance of considering signal-level contrastive learning and hypergraph learning.

Graph-oriented multi-view clustering methods have achieved impressive performances by employing relationships and complex structures hidden in multi-view data. However, most of them still suffer from the following two common problems. (1) They target at studying a common representation or pairwise correlations between views, neglecting the comprehensiveness and deeper higher-order correlations among multiple views. (2) The prior knowledge of view-specific representation can not be taken into account to obtain the consensus indicator graph in a unified graph construction and clustering framework. To deal with these problems, we propose a novel Low-rank Tensor Based Proximity Learning (LTBPL) approach for multi-view clustering, where multiple low-rank probability affinity matrices and consensus indicator graph reflecting the final performances are jointly studied in a unified framework. Specifically, multiple affinity representations are stacked in a low-rank constrained tensor to recover their comprehensiveness and higher-order correlations. Meanwhile, view-specific representation carrying different adaptive confidences is jointly linked with the consensus indicator graph. Extensive experiments on nine real-world datasets indicate the superiority of LTBPL compared with the state-of-the-art methods.

Multi-view subspace clustering targets at clustering data lying in a union of low-dimensional subspaces. Generally, an n X n affinity graph is constructed, on which spectral clustering is then performed to achieve the final clustering. Both graph construction and graph partitioning of spectral clustering suffer from quadratic or even cubic time and space complexity, leading to difficulty in clustering large-scale datasets. Some efforts have recently been made to capture data distribution in multiple views by selecting key anchor bases beforehand with k-means or uniform sampling strategy. Nevertheless, few of them pay attention to the algebraic property of the anchors. How to learn a set of high-quality orthogonal bases in a unified framework, while maintaining its scalability for very large datasets, remains a big challenge. In view of this, we propose an Efficient Orthogonal Multi-view Subspace Clustering (OMSC) model with almost linear complexity. Specifically, the anchor learning, graph construction and partition are jointly modeled in a unified framework. With the mutual enhancement of each other, a more discriminative and flexible anchor representation and cluster indicator can be jointly obtained. An alternate minimizing strategy is developed to deal with the optimization problem, which is proved to have linear time complexity w.r.t. the sample number. Extensive experiments have been conducted to confirm the superiority of the proposed OMSC method. The source codes and data are available at https://github.com/ManshengChen/Code-for-OMSC-master.

Abstract Multi-view clustering (MVC) has attracted more and more attention in the recent few years by making full use of complementary and consensus information between multiple views to cluster objects into different partitions. Although there have been two existing works for MVC survey, neither of them jointly takes the recent popular deep learning-based methods into consideration. Therefore, in this paper, we conduct a comprehensive survey of MVC from the perspective of representation learning. It covers a quantity of multi-view clustering methods including the deep learning-based models, providing a novel taxonomy of the MVC algorithms. Furthermore, the representation learning-based MVC methods can be mainly divided into two categories, i.e., shallow representation learning-based MVC and deep representation learning-based MVC, where the deep learning-based models are capable of handling more complex data structure as well as showing better expression. In the shallow category, according to the means of representation learning, we further split it into two groups, i.e., multi-view graph clustering and multi-view subspace clustering. To be more comprehensive, basic research materials of MVC are provided for readers, containing introductions of the commonly used multi-view datasets with the download link and the open source code library. In the end, some open problems are pointed out for further investigation and development.