Results 61 to 70 of about 2,491 (218)

CLusterix: A Hybrid Visualization Model for Hierarchically Clustered Networks

Computer Graphics Forum, EarlyView.
Abstract We introduce Clusterix, a novel hybrid visualization model for representing hierarchically clustered networks, which also supports directed and weighted edges. Clusterix offers an integrated view of both the network and its full cluster hierarchy by compactly visualizing the cluster inclusion tree enriched with links of the network.
Carla Binucci, Annika Bonerath, Walter Didimo, Henry Förster, Seok‐Hee Hong, Maria Eleni Pavlidi, Alessandra Tappini   +6 more
wiley   +1 more source

NE-DCHL: Nonlinear Enhanced Disentangled Contrastive Hypergraph Learning for Next Point-of-Interest Recommendation

Information
Next Point-of-Interest (POI) recommendation is a crucial task in personalized location-based services, aiming to predict the next POI that a user might visit based on their historical trajectories. Although sequence models and Graph Neural Networks (GNNs)
Hongwei Zhang, Guolong Wang, Xiaofeng Yan   +2 more
doaj   +1 more source

Learning on Partial-Order Hypergraphs

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18, 2018
Graph-based learning methods explicitly consider the relations between two entities (i.e., vertices) for learning the prediction function. They have been widely used in semi-supervised learning, manifold ranking, and clustering, among other tasks.
Fuli Feng, Xiangnan He 0001, Yiqun Liu 0001, Liqiang Nie, Tat-Seng Chua   +4 more
openaire   +1 more source

GBT‐SAM: A Parameter‐Efficient Depth‐Aware Model for Generalizable Brain Tumor Segmentation on mp‐MRI

International Journal of Imaging Systems and Technology, Volume 36, Issue 4, July 2026.
ABSTRACT Gliomas are aggressive brain tumors that require accurate imaging‐based diagnosis, where automated segmentation plays a central role in assessing tumor morphology and guiding treatment decisions. Manual delineation of gliomas is time‐consuming and prone to variability, motivating the use of deep learning to improve consistency and alleviate ...
Cecilia Diana‐Albelda, Roberto Alcover‐Couso, Álvaro García‐Martín, Jesus Bescos, Marcos Escudero‐Viñolo   +4 more
wiley   +1 more source

CF‐SBERTHet: Collaborative and Textual Knowledge Enhanced Semantic Graphs for Sparse Recommendations

Expert Systems, Volume 43, Issue 7, July 2026.
ABSTRACT Modern e‐commerce platforms face a critical challenge: delivering accurate recommendations under extreme user–item interaction sparsity, where textual context remains systematically underutilised. Existing collaborative filtering methods degrade sharply in sparse settings, while semantic approaches fail to capture collaborative patterns ...
He Ma, Maryam Khanian Najafabadi, Qiyang Wu, WeiPing Kong, Huajun Jie, Fanye Sun, Xiaoyue Bai, Mohammadhossein Ahmadi   +7 more
wiley   +1 more source

Vertex-Weighted Hypergraph Learning for Multi-View Object Classification

, 2017
3D object classification with multi-view representation has become very popular, thanks to the progress on computer techniques and graphic hardware, and attracted much research attention in recent years.
Xibin Zhao, Lifan Su, Ming Gu, Hai Wan, Yue Gao, Jiaguang Sun   +5 more
core   +1 more source

Single‐Cell and Spatial Omics: Methods and Applications

MedComm, Volume 7, Issue 4, April 2026.
Systematically summarized the breakthrough sequencing technologies and computational methods for single‐cell and spatial omics across multiple omics layers, including genome, epigenome, transcriptome, proteome, and metabolome. State‐of‐the‐art methods for multi‐omics integration, cross‐modal integration, and cross‐scale integration were reviewed, with ...
Xiaoping Cen, Xiaolan Huang, Enjin Deng, Xue Gong, Na Tan, Jifeng Ye, Yin Wang, Roland Eils, Qun Luo, Yixue Li, Fangfang Qu   +10 more
wiley   +1 more source

Bi-View Contrastive Learning with Hypergraph for Enhanced Session-Based Recommendation

Information
Session-based recommendation (SBR) aims to predict a user’s next interests based on their actions in a single visit. Recent methods utilize graph neural networks to study the pairwise relationship of item transfers, yet these often overlook the complex ...
Zijun Wang, Lai Wei
doaj   +1 more source

Relational Learning with Hypergraphs

, 2013
Keywords: relational learning ; hypergraph learning ; spectral graph theory ; recommender system ; network traffic inspection These Ecole polytechnique federale de Lausanne EPFL, n° 5807 (2013)Programme doctoral Informatique, Communications et InformationFaculte informatique et communicationsInstitut d'informatique fondamentaleLaboratoire d ...
openaire   +1 more source

A Universal Meta‐Heuristic Framework for Influence Maximisation in Hypergraphs

CAAI Transactions on Intelligence Technology, Volume 11, Issue 2, Page 396-410, April 2026.
ABSTRACT Influence maximisation (IM) aims to select a small number of nodes that are able to maximise their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective solutions in simple networks, higher‐order interactions between entities in various real‐world systems are usually not taken into ...
Ming Xie, Chuang Liu, Yang Chen, Zi‐Ke Zhang, Xiaoyang Liu, Xiu‐Xiu Zhan   +5 more
wiley   +1 more source