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, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

As communities represent similar opinions, similar functions, similar purposes, etc.,
community detection is an important and extremely useful tool in both scientific inquiry and …

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 …

Local community detection aims to find a set of densely-connected nodes containing given
query nodes. Most existing local community detection methods are designed for a single …

Community detection is a fundamental problem in machine learning. While deep learning
has shown great promise in many graphrelated tasks, developing neural models for …

Community detection has emerged during the last decade as one of the most challenging
problems in network science, which has been revisited with network representation learning …

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

Community detection is an important task in social network analysis. In community detection,
in general, there exist two types of the models that utilize either network topology or node …

Community structure is one of the most important properties of complex networks and is a
foundational concept in exploring and understanding networks. In real world, topology …