Characterizing bursty temporal interaction patterns of temporal networks is crucial to
investigate the evolution of temporal networks as well as various collective dynamics taking …

Burst patterns, characterized by their temporal heterogeneity, have been observed across a
wide range of domains, encompassing event sequences from neuronal firing to various …

The need for a method to construct multidimensional distribution function is increasing
recently, in the era of huge multiwavelength surveys. We have proposed a systematic …

Long-term temporal correlations in time series in a form of an event sequence have been
characterized using an autocorrelation function (ACF) that often shows a power-law …

Long-term temporal correlations observed in event sequences of natural and social
phenomena have been characterized by algebraically decaying autocorrelation functions …

A heterogeneous structure of social networks induces various intriguing phenomena. One of
them is the friendship paradox, which states that on average, your friends have more friends …

To characterize temporal correlations in temporal networks, we define an autocorrelation
function (ACF) for temporal networks in terms of the similarity between two snapshot …

Stochastic models in which agents interact with their neighborhood according to a network
topology are a powerful modeling framework to study the emergence of complex dynamic …

One of the interesting phenomena due to the topological heterogeneities in complex
networks is the friendship paradox, stating that your friends have on average more friends …

Complex interacting systems are ubiquitous in nature and society. Computational modeling
of these systems is, therefore, of great relevance for science and engineering. Complex …