In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

Detecting and analyzing dense groups or communities from social and information networks
has attracted immense attention over the last decade due to its enormous applicability in …

Community detection in networks receives much attention recently. Most of the previous
works are for unipartite networks composed of only one type of nodes. In real world …

A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely
used to model real-world data, such as user-item purchase records, author-article …

Connectomics and network neuroscience offer quantitative scientific frameworks for
modeling and analyzing networks of structurally and functionally interacting neurons …

Extensive microbial gene flows affect how we understand virology, microbiology, medical
sciences, genetic modification, and evolutionary biology. Phylogenies only provide a narrow …

We investigate the networked nature of the Japanese credit market. Our investigation is
performed with tools of network science. In our investigation we perform community …

Community detection in complex networks is a topic of high interest in many scientific fields.
A bipartite network is a special type of complex network whose nodes are decomposed into …

Networks are useful tools to represent and analyze interactions on a large, or genome-wide
scale and have therefore been widely used in biology. Many biological networks—such as …

In this paper, an algorithm is proposed to detect one-mode community structures in bipartite
networks, and to deduce which one-mode community structures are weighted. After …