Link-aware semi-supervised hypergraph
Nettet7. sep. 2024 · Similar to a normal graph, a hypergraph is considered as a more superior method when learning from multi-modal data, which can integrate the high-order interaction in hypergraph structure and map the correlationship among different modalities to a latent correlation matrix. Nettet12. des. 2024 · In this paper, we fill this gap by modeling session-based data as a hypergraph and then propose a dual channel hypergraph convolutional network – DHCN to improve SBR. Moreover, to enhance hypergraph modeling, we innovatively integrate self- supervised learning into the training of our network by maximizing mutual …
Link-aware semi-supervised hypergraph
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NettetHowever, existing hypergraph construction methods essentially resort to an unsupervised learning paradigm, which ignores supervisory information, such as pairwise links/non-links. In this article, to exploit the supervisory information, we propose a novel link-aware hypergraph learning model, which modulates high-order correlations of data samples … Nettet12. des. 2024 · Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation. Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role.
Nettet25. apr. 2024 · This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend … Nettet1. jan. 2024 · E. Gujral and E. E. Papalexakis. SMACD: semi-supervised multi-aspect community detection. In Proceedings of the 2024 SIAM International Conference on Data Mining, pages 702--710, 2024. Google Scholar Cross Ref; S. Günnemann, I. Färber, M. Rüdiger, and T. Seidl. SMVC: Semi-supervised multi-view clustering in subspace …
Nettet1. jan. 2024 · Link-aware semi-supervised hypergraph To construct an informative hypergraph, the key issue is to find multiple correlated samples of each sample to … Nettet9. nov. 2024 · Hypergraph is used to replace simple graph to fully explore the inherent manifold structure between labeled data and unlabeled data, and the hypergraph …
Nettetpropose a novel link-aware hypergraph learning model, which modulates high-order cor-relations of data samples in a semi-supervised manner. To construct a hypergraph, a …
Nettet16. feb. 2024 · Self-supervised Guided Hypergraph Feature Propagation for Semi-supervised Classification with Missing Node Features Chengxiang Lei, Sichao Fu, Yuetian Wang, Wenhao Qiu, Yachen Hu, Qinmu Peng, Xinge You Graph neural networks (GNNs) with missing node features have recently received increasing interest. buy healthy productsNettetIn this article, to exploit the supervisory information, we propose a novel link-aware hypergraph learning model, which modulates high-order correlations of data samples … cement block flower bedsNettet1. apr. 2024 · At present, graph regularized semi-supervised methods achieve excellent performance in various fields. However, the manifold regularization term of most … buy healthy dog food onlinebuy healthy food in bulkNettet27. jan. 2024 · To develop a flexible and effective model for graph-based semi-supervised node classification, we propose a novel Density-Aware Hyper-Graph Neural Networks … buy healthy snacks online canadaNettet27. mar. 2024 · Diffusions and label spreading are classical techniques for semi-supervised learning in the graph setting, and there are some standard ways to extend … buy healthy organic food onlineNettetLink analysis tools present data in the most tangible format for interpretation, helping users identify trends, patterns and outliers faster and more easily. User-friendly operation: … cement block maker machine