A key concept in timetabling problems is that of the conflict graph with edges representing pairs of events that are not allowed to occur at the same time. Usually, the only information presented about such graphs is their density. However, intuitively, it seems likely that such graphs are structured, and most likely have some clustering. In analysing the structure of social networks or of the ...

Abstract This study investigates the influence of lattice structure in evolutionary games. The snowdrift games is considered in networks with high clustering coefficients, that use four different strategyupdating. Analytical conjectures using pair approximation were compared with the numerical results. Results indicate that general statements asserting that the lattice structure enhances cooper...

Data mining is the mechanism of implementing patterns in large amount of data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. Clustering is the very big area in which grouping of same type of objects in data mining. Clustering has divided into different categories – partitioned clustering and hierarchical clustering. In ...

The analysis of amino acids network is very important to studying the various physicochemical properties of amino acids. In this paper we consider the amino acid network based on mutation of the codons. To analyze the relative importance of the amino acids we have discussed different measures of centrality. The measure of centrality is a powerful tool of graph theory for ranking the vertices an...

While the majority of approaches to the characterization of complex networks has relied on measurements considering only the immediate neighborhood of each network node, valuable information about the network topological properties can be obtained by considering further neighborhoods. The current work discusses on how the concepts of hierarchical node degree and hierarchical clustering coeffici...

We proposed a deterministic multidimensional growth model for small-world networks. The model can characterize the distinguishing properties of many real-life networks with geometric space structure. Our results show the model possesses small-world effect: larger clustering coefficient and smaller characteristic path length. We also obtain some accurate results for its properties including degr...

Experimental results show that in large complex networks (such as internet, social or biological networks) there exists a tendency to connect elements which have a common neighbor. In theoretical random graph models, this tendency is described by the clustering coefficient being bounded away from zero. Complex networks also have power-law degree distributions and short average distances (small ...

The capacity coefficient function is a well-established, modelbased measure comparing performance with multiple sources of information together to performance on each of those information sources in isolation. Because it is a function across time, it may contain a large amount of information about a participant. In many applications, this information has been ignored, either by using qualitativ...

The Clustering Coefficient (CC) is a fundamental measure in social network analysis assessing the degree to which nodes tend to cluster together. While CC computation on static graphs is well studied, emerging applications have new requirements for online query of the “global” CC of a given subset of a graph. As social networks are widely stored in databases for easy updating and accessing, com...