A Community Detection Algorithm on Graph Data

Networks (or Graphs) consist of two different sets to represent objects and the relationships between them. These sets are called vertex (node) and edge (link). Networks are used as an instrument to solve many scientific problems. Scientists are analysing network structure to identify certain features. As a result of the information obtained, it is possible to find out the most influential vertices or edges in the network and the clusters that the vertices create among themselves. In this study, state-of-the-art community detection algorithms offered by igraph (a network analysis and visualization tool) will be described. In addition, a new approach is proposed which defines the sub-graphs as a community that will occur as a result of removing the bridges on the graphs.