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 ...

Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...

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, ...

Large-scale semi-supervised annotation (self-learning, co-training) for text (code for papers @ KDD17, @ KAIS19); Repository maintained by Vasileios Iosifidis.

Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods ...

Wright, O., 2019: The Promise of Deep Learning on Graphs. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed December 9, 2025 ...

Abstract: Graph-based methods play an important role in unsupervised and semi-supervised learning tasks by taking into account the underlying geometry of the data set. In this paper, we consider a ...