A comprehensive survey on graph anomaly detection with deep learning
Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …
the others in the sample. Over the past few decades, research on anomaly mining has …
A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions
Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …
groups so that similar samples belong to the same cluster while dissimilar samples belong …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Edge computing for internet of everything: A survey
In this era of the Internet of Everything (IoE), edge computing has emerged as the critical
enabling technology to solve a series of issues caused by an increasing amount of …
enabling technology to solve a series of issues caused by an increasing amount of …
MGLNN: Semi-supervised learning via multiple graph cooperative learning neural networks
In many machine learning applications, data are coming with multiple graphs, which is
known as the multiple graph learning problem. The problem of multiple graph learning is to …
known as the multiple graph learning problem. The problem of multiple graph learning is to …
Community detection algorithms in healthcare applications: a systematic review
Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …
efraudcom: An e-commerce fraud detection system via competitive graph neural networks
With the development of e-commerce, fraud behaviors have been becoming one of the
biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking …
biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking …
Fraudre: Fraud detection dual-resistant to graph inconsistency and imbalance
The objective of fraud detection is to distinguish fraudsters from normal users. In
graph/network environments, both fraudsters and normal users are modeled as nodes, and …
graph/network environments, both fraudsters and normal users are modeled as nodes, and …
Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding
The identification of protein complexes in protein-protein interaction networks is the most
fundamental and essential problem for revealing the underlying mechanism of biological …
fundamental and essential problem for revealing the underlying mechanism of biological …
An improved influence maximization method for social networks based on genetic algorithm
Over the recent decade, much research has been conducted in the field of social networks.
The structure of these networks has been irregular, complex, and dynamic, and certain …
The structure of these networks has been irregular, complex, and dynamic, and certain …