Signal propagation in complex networks
Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …
going viral, promotes trust and facilitates moral behavior in social groups, enables the …
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 …
Unsupervised deep anomaly detection for multi-sensor time-series signals
Nowadays, multi-sensor technologies are applied in many fields, eg, Health Care (HC),
Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can …
Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can …
Learning representations for time series clustering
Time series clustering is an essential unsupervised technique in cases when category
information is not available. It has been widely applied to genome data, anomaly detection …
information is not available. It has been widely applied to genome data, anomaly detection …
End-to-end deep representation learning for time series clustering: a comparative study
Time series are ubiquitous in data mining applications. Similar to other types of data,
annotations can be challenging to acquire, thus preventing from training time series …
annotations can be challenging to acquire, thus preventing from training time series …
A comprehensive survey of the key technologies and challenges surrounding vehicular ad hoc networks
Vehicular ad hoc networks (VANETs) and the services they support are an essential part of
intelligent transportation. Through physical technologies, applications, protocols, and …
intelligent transportation. Through physical technologies, applications, protocols, and …
Multiview unsupervised shapelet learning for multivariate time series clustering
N Zhang, S Sun - IEEE Transactions on Pattern Analysis and …, 2022 - ieeexplore.ieee.org
Multivariate time series clustering has become an important research topic in the time series
learning task, which aims to discover the correlation among multiple sequences and …
learning task, which aims to discover the correlation among multiple sequences and …
A review and evaluation of elastic distance functions for time series clustering
Time series clustering is the act of grouping time series data without recourse to a label.
Algorithms that cluster time series can be classified into two groups: those that employ a time …
Algorithms that cluster time series can be classified into two groups: those that employ a time …
Multi-label correlation guided feature fusion network for abnormal ECG diagnosis
Electrocardiographic (ECG) abnormalities are the most intuitive manifestation in the clinical
diagnosis of cardiovascular disease. Although significant progress has been achieved in …
diagnosis of cardiovascular disease. Although significant progress has been achieved in …