Deep learning (DL) has attracted increasing attention on account of its significant processing
power in tasks, such as speech, image, or text processing. In order to the exponential …

Anomaly detection has evolved as a successful research subject in the areas such as
bibliometrics, informatics and computer networks including security-based and social …

Abstract Heterogeneous Information Networks (HINs) are ubiquitous in the real world, and
discovering anomalies is essential for understanding network semantics through nodes and …

Background Anomaly detection is crucial in healthcare data due to challenges associated
with the integration of smart technologies and healthcare. Anomaly in electronic health …

摘要近年来, 我国科学技术发展态势已进入“跟跑, 并跑, 领跑” 三跑并存的阶段,
为适应新的科技发展形势, 开展重点领域的科技前沿识别, 全面跟踪国外主要国家科技发展新 …

Background Healthcare programs and insurance initiatives play a crucial role in ensuring
that people have access to medical care. There are many benefits of healthcare insurance …

In heterogeneous graph analysis, existing self-supervised learning (SSL) methods face
several key challenges. Primarily, these approaches are tailored for node-level tasks and fail …

Abstract Machine learning has seized both academia and industry's attention as deep
learning (DL) is the frontrunner in data science. In order to construct computational models …

The problem of anomaly detection on static networks has been broadly studied in various
research domains. Anomaly detection concerns the identification of objects or connections …

Journal Impact Factor is a popular metric for determining the quality of a journal in
academia. The number of citations received by a journal is a crucial factor in determining the …