Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …

Causal network reconstruction from time series is an emerging topic in many fields of
science. Beyond inferring directionality between two time series, the goal of causal network …

Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

Information flow or information transfer the widely applicable general physics notion can be
rigorously derived from first principles, rather than axiomatically proposed as an ansatz. Its …

Most of the data-driven satellite telemetry data anomaly detection methods suffer from high
false positive rate (FPR) and poor interpretability. To solve the above problems, we propose …

The broad abundance of time series data, which is in sharp contrast to limited knowledge of
the underlying network dynamic processes that produce such observations, calls for a …

Causality analysis is an important problem lying at the heart of science, and is of particular
importance in data science and machine learning. An endeavor during the past 16 years …

A central task in analyzing complex dynamics is to determine the loci of information storage
and the communication topology of information flows within a system. Over the last decade …

Recently, a rigorous yet concise formula was derived to evaluate information flow, and
hence the causality in a quantitative sense, between time series. To assess the importance …