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

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

Graph clustering assigns nodes into tightly connected groups known as communities, and
community detection algorithms traditionally focus on non-attributed networks that only …

Community detection has received a great deal of attention, since it could help to reveal the
useful information hidden in complex networks. Although most previous modularity-based …

Community detection is one of the most popular researches in a variety of complex systems,
ranging from biology to sociology. In recent years, there's an increasing focus on the rapid …

Community structures can reveal organizations and functional properties of complex
networks; hence, detecting communities from networks is of great importance. With the surge …

In recent years, attributed networks are increasingly available for us. How to leverage
attribute information to gain a better performance of community detection has attracted …

Many real-world networks are characterized by well-defined statistics of overlapping and
nested communities. This paper proposes a series of seed-extension-based, overlapping …