What do you mean by community detection?
Community detection, also called graph partition, helps us to reveal the hidden relations among the nodes in the network. Many algorithms have been developed to detect communities (Clauset et al., 2004; Girvan and Newman, 2002; Lancichinetti and Fortunato, 2009).
How do you identify communities on a social network graph?
Another method to detect communities is by simulating random walks inside the network. It is based on the principle that a random walker will tend to stay inside densely connected areas of the graph. That is the idea behind the walktrap algorithm, introduced by Pascal Pons and Matthieu Latapy in 2005.
What are social and community networks?
The features that distinguish a social network from a community are that people are held together by pre-established personal relationships, and the connections are built one at a time. Think about your own social network and how sites like Facebook mirror that set of connections.
What are the structures of a community?
Community structure means the internal structure of an employment area, town, city, neighbourhood or another urban area. It includes the population and housing, jobs and production, service and leisure time areas, along with transport routes and technical networks, their location and relationships.
What is structure of a community?
How are communities detected in a social network?
Since networks are usually mod- eled as graphs, detecting communities in multifarious networks is also known as the graph partition problem in modern graph theory [7,2], as well as the graph clustering [1] or dense subgraph discov- ery problem [16] in the graph mining area.
Who is the author of community detection [ 8 ]?
Community detection [8] aims at grouping nodes in accordance with the relationships among them to form strongly linked sub- Corresponding author: Chaokun Wang. This work is licensed under the Creative Commons Attribution- NonCommercial-NoDerivs 3.0 Unported License.
Is there a family of community detection approaches?
The extensive research work has promoted the prosperity of the family of community detection approaches. However, it also raises a new difficulty, how to choose the most appropriate approach in specific scenarios, since many latest approaches have not been com- pared with each other upon unified platforms with same datasets and uniform configurations.
How is community de-tection studied in social networks?
We engage in an in-depth benchmarking study of community de- tection in social networks. We formulate a generalized community detection procedure and propose a procedure-oriented framework for benchmarking.