Detecting the overlapping and hierarchical community structure of complex networks

Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling to investigate different hierarchical levels of organization. Tests on real and artificial networks give excellent results.Comment: 20 pages, 8 figures. Final version published on New Journal of Physic

Quantifying and identifying the overlapping community structure in networks

It has been shown that the communities of complex networks often overlap with each other. However, there is no effective method to quantify the overlapping community structure. In this paper, we propose a metric to address this problem. Instead of assuming that one node can only belong to one community, our metric assumes that a maximal clique only belongs to one community. In this way, the overlaps between communities are allowed. To identify the overlapping community structure, we construct a maximal clique network from the original network, and prove that the optimization of our metric on the original network is equivalent to the optimization of Newman's modularity on the maximal clique network. Thus the overlapping community structure can be identified through partitioning the maximal clique network using any modularity optimization method. The effectiveness of our metric is demonstrated by extensive tests on both the artificial networks and the real world networks with known community structure. The application to the word association network also reproduces excellent results.Comment: 9 pages, 7 figure

Complex Networks as Hypergraphs

The representation of complex systems as networks is inappropriate for the study of certain problems. We show several examples of social, biological, ecological and technological systems where the use of complex networks gives very limited information about the structure of the system. We propose to use hypergraphs to represent these systems by introducing the concept of the complex hyper-network. We define several structural measures for complex hyper-networks. These measures characterize hyper-network structures on the basis of node participation in different hyper-edges (groups) and sub-hypergraphs. We also define two clustering coefficients, one characterizing the transitivity in the hyper-network through the proportion of hyper-triangles to paths of length two and the other characterizing the formation of triples of mutually adjacent groups in the hyper-network. All of these characteristics are studied in two different hyper-networks; a scientific collaboration hyper-network and an ecological competence hyper-network.Comment: 16 pages, 3 figure

Impact of volatile phenols and their precursors on wine quality and control measures of Brettanomyces/Dekkera yeasts

Volatile phenols are aromatic compounds and one of the key molecules responsible for olfactory defects in wine. The yeast genus Brettanomyces is the only major microorganism that has the ability to covert hydroxycinnamic acids into important levels of these compounds, especially 4-ethylphenol and 4-ethylguaiacol, in red wine. When 4-ethylphenols reach concentrations greater than the sensory threshold, all wine’s organoleptic characteristics might be influenced or damaged. The aim of this literature review is to provide a better understanding of the physicochemical, biochemical, and metabolic factors that are related to the levels of p-coumaric acid and volatile phenols in wine. Then, this work summarizes the different methods used for controlling the presence of Brettanomyces in wine and the production of ethylphenols

Formation d'arome par hydrolyse de precurseurs: application au raisin

National audienc

Evolution de l'arôme au cours de la conservation du vin : formation de 4-(1-éthoxyéthyl)-phénol et 4-(1-éthoxyéthyl)-gaïacol

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Arômes indésirables inductibles par l'usage de préparations enzymatiques en vinification : mise en évidence et analyse

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Intervention des preparations enzymatiques sur la formation de phenols volatils au cours de la vinification

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Etude de l'origine du citronellol dans les vins

National audienc

Action des glycosidases exogenes au cours de la vinification: liberation de l'arome a partir des precurseurs glycosidiques

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