Bibliometric approach to community discovery

We propose an efficient and novel approach for discovering communities in real-world random networks. Communities are formed by subsets of nodes in a graph, which are closely related. Extraction of these communities facilitates better understanding of ...

Many real-world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature ...

Most approaches for mining the community structure of a graph are based on the assumption that each member of a community has more links within than outside its community. We argue that this delimitation of a community is not appropriate for graphs ...