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 …

With the rapid development of information technologies, various big graphs are prevalent in
many real applications (eg, social media and knowledge bases). An important component of …

Recently, the topic of community search (CS) has gained plenty of attention. Given a query
vertex, CS looks for a dense subgraph that contains it. Existing studies mainly focus on …

We consider the community search problem defined upon a large graph G: given a query
vertex q in G, to find as output all the densely connected subgraphs of G, each of which …

Bipartite graphs are widely used to model relation-ships between two types of entities.
Community search retrieves densely connected subgraphs containing a query vertex, which …

Community detection is an important task with many applications. However, there is no
universal definition of communities, and a variety of algorithms have been proposed based …

Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been
studied for decades, and is widely used in various areas, including network science …

The problem of k-truss search has been well defined and investigated to find the highly
correlated user groups in social networks. But there is no previous study to consider the …

Community search enables personalized community discovery and has wide applications in
large real-world graphs. While community search has been extensively studied for …

Attributed community search aims to find the community with strong structure and attribute
cohesiveness from attributed graphs. However, existing works suffer from two major …