Temporal link prediction: A unified framework, taxonomy, and review
Dynamic graphs serve as a generic abstraction and description of the evolutionary
behaviors of various complex systems (eg, social networks and communication networks) …
behaviors of various complex systems (eg, social networks and communication networks) …
A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data
Nowadays, multivariate time series data are increasingly collected in various real world
systems, eg, power plants, wearable devices, etc. Anomaly detection and diagnosis in …
systems, eg, power plants, wearable devices, etc. Anomaly detection and diagnosis in …
Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks
Massive and dynamic networks arise in many practical applications such as social media,
security and public health. Given an evolutionary network, it is crucial to detect structural …
security and public health. Given an evolutionary network, it is crucial to detect structural …
Anomalydae: Dual autoencoder for anomaly detection on attributed networks
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate
significantly from the majority of reference nodes, which is pervasive in many applications …
significantly from the majority of reference nodes, which is pervasive in many applications …
Localizing failure root causes in a microservice through causality inference
An increasing number of Internet applications are applying microservice architecture due to
its flexibility and clear logic. The stability of microservice is thus vitally important for these …
its flexibility and clear logic. The stability of microservice is thus vitally important for these …
Interactive anomaly detection on attributed networks
Performing anomaly detection on attributed networks concerns with finding nodes whose
patterns or behaviors deviate significantly from the majority of reference nodes. Its success …
patterns or behaviors deviate significantly from the majority of reference nodes. Its success …
A spatiotemporal deep learning approach for unsupervised anomaly detection in cloud systems
Anomaly detection is a critical task for maintaining the performance of a cloud system. Using
data-driven methods to address this issue is the mainstream in recent years. However, due …
data-driven methods to address this issue is the mainstream in recent years. However, due …
Structural temporal graph neural networks for anomaly detection in dynamic graphs
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications
in areas such as security, finance, and social media. Existing network embedding based …
in areas such as security, finance, and social media. Existing network embedding based …
Causal inference-based root cause analysis for online service systems with intervention recognition
Fault diagnosis is critical in many domains, as faults may lead to safety threats or economic
losses. In the field of online service systems, operators rely on enormous monitoring data to …
losses. In the field of online service systems, operators rely on enormous monitoring data to …
Incremental causal graph learning for online root cause analysis
The task of root cause analysis (RCA) is to identify the root causes of system faults/failures
by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure …
by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure …