Results 101 to 110 of about 5,865 (183)

Multi-View Contrastive Fusion POI Recommendation Based on Hypergraph Neural Network

Mathematics
In the era of information overload, location-based social software has gained widespread popularity, and the demand for personalized POI (Point of Interest) recommendation services is growing rapidly.
Luyao Hu, Guangpu Han, Shichang Liu, Yuqing Ren, Xu Wang, Ya Liu, Junhao Wen, Zhengyi Yang   +7 more
doaj   +1 more source

SSRES: A Student Academic Paper Social Recommendation Model Based on a Heterogeneous Graph Approach

Mathematics
In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information.
Yiyang Guo, Zheyu Zhou
doaj   +1 more source

Dual convolutional network based on hypergraph and multilevel feature fusion for road extraction from high-resolution remote sensing images

International Journal of Digital Earth
Road extraction from high-resolution remote sensing images (HRSI) is confronted with the challenge that roads are occluded by other objects, including opaque obstructions and similarly colored areas. This paper proposes a dual convolutional network based
BoWen Li, XiangHong Tang, Rang Xiao, JianGuang Lu, YuHao Wang   +4 more
doaj   +1 more source

Robust Financial Fraud Detection via Causal Intervention and Multi-View Contrastive Learning on Dynamic Hypergraphs

Mathematics
Financial fraud detection is critical to modern economic security, yet remains challenging due to collusive group behavior, temporal drift, and severe class imbalance.
Xiong Luo
doaj   +1 more source

Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation

Knowledge hypergraphs generalize knowledge graphs using hyperedges to connect multiple entities and depict complicated relations. Existing methods either transform hyperedges into an easier-to-handle set of binary relations or view hyperedges as isolated and ignore their adjacencies. Both approaches have information loss and may potentially lead to the
Li, Mengfan, Shi, Xuanhua, Qiao, Chenqi, Zhang, Teng, Jin, Hai   +4 more
openaire   +2 more sources

DPHGNN: A Dual Perspective Hypergraph Neural Networks

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Accepted in SIGKDD'24 -- Research ...
Siddhant Saxena, Shounak Ghatak, Raghu Kolla, Debashis Mukherjee, Tanmoy Chakraborty   +4 more
openaire   +2 more sources

A Review of Hypergraph Neural Networks

EAI Endorsed Transactions on e-Learning
In recent years, Graph Neural Networks (GNNs) have seen notable success in fields such as recommendation systems and natural language processing, largely due to the availability of vast amounts of data and powerful computational resources. GNNs are primarily designed to work with graph data that involve pairwise relationships.
openaire   +1 more source

DGHNN: a deep graph and hypergraph neural network for pan-cancer related gene prediction. [PDF]

Bioinformatics
Li B, Xiao X, Zhang C, Xiao M, Zhang L.
europepmc   +1 more source

Integration of single cell multiomics data by deep transfer hypergraph neural network. [PDF]

Brief Funct Genomics
Kan Y, Zhang Z, Wang Y, Qi Y, Chang H, Wang W, Zhang Z, Liu Q, Shi X.   +8 more
europepmc   +1 more source