There are considerable interest in algorithms for detecting community structure, which is fundamental for analyzing the relationship between structure and function in complex networks. In this paper, after the introduction of some traditional approaches for detecting community structure and data mining clustering algorithms, we propose Mapping Vertex into Vector(MVV) algorithm, which can conver...

Clustering is a frequently used tool in the analysis and evaluation of large and complex networks. Most such networks result from or model dynamic processes, thus clustering techniques need to be adapted. We provide a model for clustering graphs that are subject to changes. More precisely, we address the update problem, i. e., maintaining the clustering while nodes and edges can be inserted and...

AIM Enterobacter cloacae complex bacteria are of both clinical and environmental importance. Phenotypic methods are unable to distinguish between some of the species in this complex, which often renders their identification incomplete. The goal of this study was to develop molecular assays to identify Enterobacter hormaechei and Ent. cloacae genetic cluster III which are relatively frequently e...

Herein we describe the synthesis, structural and magnetic characterisation of three transition metal cluster complexes that feature the polytopic ligand N-(2-pyridyl)-3-carboxypropanamide (H2L): [Fe3(III)Fe2(II)(HL)6(O)(H2O)3][ClO4]5·3MeCN·4H2O, 1, [Co8(HL)8(O)(OH)4(MeOH)3(H2O)]-[ClO4]3·5MeOH·2H2O, 2, and [Cu6(L(ox))4(MeOH)(H2O)3]·MeOH, 3. Complex 1 is a mixed valence penta-nuclear iron cluster...

Financial markets are a typical example of complex systems where interactions between constituents lead to many remarkable features. Here we give empirical evidence, by making as few assumptions as possible, that the market microstructure capturing almost all of the available information in the data of stock markets does not involve higher order than pairwise interactions. We give an economic i...

An efficient and relatively fast algorithm for the detection of communities in complex networks is introduced. The method exploits spectral properties of the graph Laplacian matrix combined with hierarchical clustering techniques, and includes a procedure for maximizing the ‘modularity’ of the output. Its performance is compared with that of other existing methods, as applied to different well-...

This paper presents two clustering methods: the first one uses a density-based approach (DGC) and the second one uses a frequent itemset mining approach (FINN). DGC uses regulation information as well as order preserving ranking for identifying relevant clusters in gene expression data. FINN exploits the frequent itemsets and uses a nearest neighbour approach for clustering gene sets. Both the ...

Owing to the sheer size of large and complex networks, it is necessary to reduce the information to describe essential properties of vertices and edges, regions, or the whole graph. Usually this is done via network statistics, i.e., a single number, or a series of numbers, catching the relevant and needed information. In this chapter we will give a list of statistics which are not covered in ot...

Fuzzy modelling has interpretability of the obtained models as a fundamental goal. In this paper a control-oriented local-model fuzzy clustering algorithm will try that local models approximate the linearized plant model on their validity zones. A family of clustering algorithms is presented so that it incorporates some desirable characteristics regarding convexity and smoothness of the final i...