Abstract
The network sciences have provided significant strides for understanding complex systems. Those systems are represented by graphs. One of the most relevant features of graphs representing real systems is clustering, or community structure. The communities are clusters (groups) of nodes, with more edges connecting to nodes of the same cluster and comparatively fewer edges connecting to nodes of different clusters. It can be considered as independent compartments of a graph. There are two possible sources of information we can use for the community detection: the network structure, and the attributes and features of nodes. In this paper, we use the features of nodes to detect communities. There are nodes in network that are more able and susceptible to diffuse information and propagate influence. The main purpose of our approach is to find leader nodes of networks and to form community around those nodes. Unlike to most existing researches studies, the proposed algorithm doesn’t require a priori knowledge of k number of communities to be detected.
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Ahajjam, S., Badir, H., Fissoune, R., El Haddad, M. (2015). Communities Identification Using Nodes Features. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_33
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DOI: https://doi.org/10.1007/978-3-319-25252-0_33
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