![]() This type of local algorithm could open the way to applications to large-scale technological and biological systems.įor the other class of algorithms, called divisive, the order of construction of the tree is reversed: one starts with the whole graph and iteratively cuts the edges, thus dividing the network progressively into smaller and smaller disconnected subnetworks identified as the communities. In particular, we show how the algorithm applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods. The algorithm is tested on artificial and real-world graphs. Furthermore, we propose a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability. In this way the algorithms for the identification of the community structure become fully self-contained. In this article we deal with this problem by showing how quantitative definitions of community are implemented in practice in the existing algorithms. Several types of algorithms exist for revealing the community structure in networks, but a general and quantitative definition of community is not implemented in the algorithms, leading to an intrinsic difficulty in the interpretation of the results without any additional nontopological information. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic and protein networks), or technological problems (optimization of large infrastructures). The investigation of community structures in networks is an important issue in many domains and disciplines.
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