In the study of complex networks, a network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally. In the particular case of non-overlapping community finding, this implies that the network divides naturally into groups of nodes with dense connections internally and sparser connections between groups. But overlapping communities are also allowed. The more general definition is based on the principle that pairs of nodes are more likely to be connected if they are both members of the same community(ies), and less likely to be connected if they do not share communities. A related but different problem is community search, where the goal is to find a community that a certain vertex belongs to.
In the study of networks, such as computer and information networks, social networks and biological networks, a number of different characteristics have been found to occur commonly, including the small-world property, heavy-tailed degree distributions, and clustering, among others. Another common characteristic is community structure.
In the context of networks, community structure refers to the occurrence of groups of nodes in a network that are more densely connected internally than with the rest of the network, as shown in the example image to the right. This inhomogeneity of connections suggests that the network has certain natural divisions within it.
Communities are often defined in terms of the partition of the set of vertices, that is each node is put into one and only one community, just as in the figure. This is a useful simplification and most community detection methods find this type of community structure. However, in some cases a better representation could be one where vertices are in more than one community. This might happen in a social network where each vertex represents a person, and the communities represent the different groups of friends: one community for family, another community for co-workers, one for friends in the same sports club, and so on.
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En sciences humaines et sociales, l'expression réseau social désigne un agencement de liens entre des individus ou des organisations, constituant un groupement qui a un sens : la famille, les collègues, un groupe d'amis, une communauté, etc. L'anthropologue australien John Arundel Barnes a introduit l'expression en 1954. L'analyse des réseaux sociaux est devenue une spécialité universitaire dans le champ de la sociologie, se fondant sur la théorie des réseaux et l'usage des graphes.
vignette|Les liens de la network science La Science des Réseaux, ou Network Science, est une discipline scientifique émergente qui se donne pour objet l'étude des relations, liens et interconnexions entre les choses, et non les choses en elles-mêmes. Champ interdisciplinaire de recherche, elle s'applique en physique, biologie, épidémiologie, science de l'information, science cognitive et réseaux sociaux. Elle vise à découvrir des propriétés communes au comportement de ces réseaux hétérogènes via la construction d'algorithmes et d'outils.
In the study of complex networks, a network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally. In the particular case of non-overlapping community finding, this implies that the network divides naturally into groups of nodes with dense connections internally and sparser connections between groups. But overlapping communities are also allowed.
Explore les structures fondamentales des réseaux de communication dynamiques, y compris la prévisibilité dans la mobilité humaine et les communautés de réseaux.
Couvre le modèle de bloc stochastique pour la détection communautaire.
Explore la théorie de la coloration des graphes, le regroupement spectral, la détection de communauté et les structures de réseau.
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Community structure in graph-modeled data appears in a range of disciplines that comprise network science. Its importance relies on the influence it bears on other properties of graphs such as resilie
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In many applications, a dataset can be considered as a set of observed signals that live on an unknown underlying graph structure. Some of these signals may be seen as white noise that has been filter