Knowledge graphs for the life sciences: Recent developments, challenges and opportunities
The term life sciences refers to the disciplines that study living organisms and life processes,
and include chemistry, biology, medicine, and a range of other related disciplines. Research …
and include chemistry, biology, medicine, and a range of other related disciplines. Research …
Hot spots and trends in the relationship between cancer and obesity: a systematic review and knowledge graph analysis
L Gao, T Yang, Z Xue, CKD Chan - Life, 2023 - mdpi.com
Cancer is one of the most difficult medical problems in today's world. There are many factors
that induce cancer in humans, and obesity has become an important factor in inducing …
that induce cancer in humans, and obesity has become an important factor in inducing …
Low-dimensional federated knowledge graph embedding via knowledge distillation
Federated Knowledge Graph Embedding (FKGE) aims to facilitate collaborative learning of
entity and relation embeddings from distributed Knowledge Graphs (KGs) across multiple …
entity and relation embeddings from distributed Knowledge Graphs (KGs) across multiple …
Unsupervised Anomaly Detection on Attributed Networks With Graph Contrastive Learning for Consumer Electronics Security
The proliferation of consumer electronic products has engendered a substantial surge in
data generation and information exchange, concurrently escalating the potential for security …
data generation and information exchange, concurrently escalating the potential for security …
FGTL: Federated Graph Transfer Learning for Node Classification
Unsupervised multi-source domain transfer in federated scenario has become an emerging
research direction, which can help unlabeled target domain to obtain the adapted model …
research direction, which can help unlabeled target domain to obtain the adapted model …
GCL-Leak: Link Membership Inference Attacks against Graph Contrastive Learning
X Wang, WH Wang - Proceedings on Privacy Enhancing …, 2024 - petsymposium.org
Graph contrastive learning (GCL) has emerged as a successful method for self-supervised
graph learning. It involves generating augmented views of a graph by augmenting its edges …
graph learning. It involves generating augmented views of a graph by augmenting its edges …