Community detection is one of the main research topics in network analysis. Most network
data reveal a certain structural relationship between nodes and provide attributes describing …

Community detection involves grouping the nodes of a network such that nodes in the same
community are more densely connected to each other than to the rest of the network …

Community detection based on spectral clustering has been proved effective. However,
spectral clustering is more challenging due to two significant issues: the construction of a …

Community structure in complex networks has been proven to be valuable in a variety of
fields, such as biology, social media, health, etc. Researchers have investigated a significant …

An important problem in network analysis is to identify significant communities. Most of the
real-world data sets exhibit a certain topological structure between nodes and the attributes …

Community structure in networks indicates groups of vertices within which are dense
connections and between which are sparse connections. Community detection, an important …

These days, there are many types of complex networks that understanding the topology and
functions of them allows us to derive valuable information from these networks. In this …

Community detection using statistical models is a promising research area in network
analysis. Most existing statistical models for this task cannot be fitted well for various …

Recent research on community detection focuses on learning representations of nodes
using different network embedding methods, and then feeding them as normal features to …

Community detection in networks plays a key role in understanding their structures, and the
application of clustering algorithms in community detection tasks in complex networks has …