Community detection refers to the task of discovering groups of vertices sharing similar
properties or functions so as to understand the network data. With the recent development of …

In-depth exploration of the knowledge linkages between science and technology (S&T) is an
essential prerequisite for accurately understanding the S&T innovation laws, promoting the …

A plethora of exhaustive studies have proved that the community detection merely based on
topological information often leads to relatively low accuracy. Several approaches aim to …

Recommender systems play an important role in our life, which would help users to find
what they are interested in. Collaborative filtering is the most widely used and successful …

The study of community structure in complex networks has always been a subject of great
concern in various fields. Community structure can reflect the dynamic characteristics and …

Complex networks are widely used in the research of social and biological fields. Analyzing
real community structure in networks is the key to the study of complex networks. Modularity …

Community detection from networks is an emerging topic in modern network science.
Communities are defined as clusters of nodes or vertices that share higher concentration of …

This paper presents a new methodology for characterising the evolving behaviour of the
time-varying causality between multivariate time series, from the perspective of change in …

Community structures are ubiquitous in various complex networks, implying that the
networks commonly be composed of groups of nodes with more internal links and less …

Detecting communities in complex networks has been one of the most popular research
areas in recent years. There have been many community detection algorithms proposed to …