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 is a fundamental and widely-studied problem that finds all densely-
connected groups of nodes and well separates them from others in graphs. With the …

As a fundamental task in cluster analysis, community detection is crucial for the
understanding of complex network systems in many disciplines such as biology and …

Attributed networks consist of not only a network structure but also node attributes. Most
existing community detection algorithms only focus on network structures and ignore node …

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 …

Community detection aims to partition network nodes into a set of clusters, such that nodes
are more densely connected to each other within the same cluster than other clusters. For …

In recent years, many attributednetwork have emerged, such as Facebook networks in social
networks, protein networks and academic citation networks. In order to find communities …

Community detection is an important problem for understanding the structure and function of
complex networks and has attracted a lot of attention in recent decades. Most community …

Community detection is a challenging task in attributed networks, due to the data
inconsistency between network topological structure and node attributes. The problem of …

Graph clustering techniques are very useful for detecting densely connected groups in large
graphs. Many existing graph clustering methods mainly focus on the topological structure …