The increasing availability of temporal network data is calling for more research on
extracting and characterizing mesoscopic structures in temporal networks and on relating …

In the study of networks, it is often insightful to use algorithms to determine mesoscale
features such as 'community structure', in which densely connected sets of nodes constitute …

In recent years, with the rapid growth of social network services (SNS), social networks
pervade nearly every aspect of our daily lives. Social networks are influencing today's …

Background Proteins dynamically interact with each other to perform their biological
functions. The dynamic operations of protein interaction networks (PPI) are also reflected in …

Nowadays, there are usually a huge number of sensors in most of the Internet of Things. In
this case, if a large number of nodes in the network transmit information at the same time, the …

We present a new method, State-based Dynamic Community Structure, that detects time-
dependent community structure in networks of brain regions. Most analyses of functional …

In this paper, a label boundary node algorithm (LBN) based on boundary nodes and label
propagation is proposed. The algorithm reduces the update randomness in label …

In this work, we consider the problem of combining link, content and temporal analysis for
community detection and prediction in evolving networks. Such temporal and content-rich …

Communities are essential building blocks of complex networks enjoying significant
research attention in terms of modeling and detection algorithms. Common across models is …

In this paper I introduce a framework for modeling temporal communication networks and
dynamical processes unfolding on such networks. The framework originates from the (new) …