Community structure is ubiquitous in real-world complex networks. The task of community
detection over these networks is of paramount importance in a variety of applications …

Community structure is the most significant attribute of networks, which is often identified to
help discover the underlying organization of networks. Currently, nonnegative matrix …

Many complex systems are composed of coupled networks through different layers, where
each layer represents one of many possible types of interactions. A fundamental question is …

Community detection is one of the popular research topics in the field of complex networks
analysis. It aims to identify communities, represented as cohesive subgroups or clusters …

Community detection or graph clustering is crucial to understanding the structure of complex
networks and extracting relevant knowledge from networked data. Latent factor model, eg …

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 …

Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Community detection methods based on attribute network representation learning are
receiving increasing attention. However, few existing works are focused exclusively on …

Community detection has become an important research topic in machine learning due to
the proliferation of network data. However, most existing methods have been developed …

Community detection, aiming at determining correct affiliation of each node in a network, is a
critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non …