IEEE

CamFD: Semi-Supervised Camouflage-Aware Fraud Detection Based on Dynamic Graphs

Abstract: Fraud detection on dynamic graph (FDDG) is in high demand across many real-world applications, such as financial transaction networks and social networks. A graph neural network (GNN) is an ...
IEEE

A Convex Formulation for Fast Semi-supervised Learning

Abstract: As a compromise between supervised and unsupervised learning, semi-supervised learning (SSL) harnesses both labeled and unlabeled data to enhance learning performance. Graph-based ...
Scientific Research Publishing

Perin, E. L. S., De Souza, M. C., De Andrade Silva, J., & Matsubara, E. T. (2025). DynGraph-BERT: Combining BERT and GNN Using Dynamic Graphs for Inductive Semi-Supervised Text ...

ABSTRACT: Government procurement contracts can be complicated, with extensive risk analysis and compliance reviews. The traditional methods of contract analytics are time-consuming and often inexact, ...
Frontiers

stGuide advances label transfer in spatial transcriptomics through attention-based supervised graph representation learning

The growing availability of spatial transcriptomics data offers key resources for annotating query datasets using reference datasets. However, batch effects, unbalanced reference annotations, and ...
Frontiers

A transformation uncertainty and multi-scale contrastive learning-based semi-supervised segmentation method for oral cavity-derived cancer

Objectives: Oral cavity-derived cancer pathological images (OPI) are crucial for diagnosing oral squamous cell carcinoma (OSCC), but existing deep learning methods for OPI segmentation rely heavily on ...
Scientific Research Publishing

Semi-Supervised Stochastic Configuration Networks Based on Manifold Regularization Framework ()

1 School of Mathematics and Statistics, Guilin University of Technology, Guilin, China. 2 Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin, China. The stochastic ...