Community search
Discovering communities in a network, known as community detection/discovery, is a fundamental problem in network science, which attracted much attention in the past several decades. In recent years, with the tremendous studies on big data, another related but different problem, called community search, which aims to find the most likely community that contains the query node, has attracted great attention from both academic and industry areas. It is a query-dependent variant of the community detection problem. A detailed survey of community search can be found at ref., which reviews all the recent studies Main advantages. As  pointed by the first work on community search published in SIGKDD'2010, many existing community detection/discovery methods consider the "static" community detection problem, where the graph needs to be partitioned a-priori with no reference to query nodes. While community search often focuses the most-likely communitie containing the query vertex. The main advantages of community search over community detection/discovery are listed as below: For example, a recent work, which focuses on attributed graphs, where nodes are often associated with some attributes like keyword, and tries to find the communities, called attributed communities, which exhibit both strong structure and  keyword cohesiveness. The query users are allowed to specify a query node and some other query conditions: (1) a value, k, the minimum degree for the expected communities; and (2) a set of keywords, which control the semantic of the expected communities. The communities returned can be easily interpreted by the keywords shared by all the community members. More details can be fround from. Some recent studies have shown that, for million-scale graphs, community search often takes less than 1 second to find a well-defined community, which is generally much faster than many existing community detection/discovery methods . This also implies that, community search is more suitable for finding communities from big graphs. Metrics for community search. Community search often uses some well-defined, fundamental graph metrics to formulate the cohesiveness of communities. The commonly used metrics are k-core (minimum degree), k-truss, k-edge-connected , etc. Among these measures, the k-core metric is the most popular one, and has been used in many recent studies as surveyed in.