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Evolving communities in networks
Minghui Jiang, Computer Science, 2010 1
Chapter 1: Introduction
1.1 Background
Large quantity of systems in the real world in which people live can be expressed ascomplex network, more over, these large quantity of complex 代写留学生论文systems are alsodynamic, such as society network, co-actor network, Email network, neural network,co-authorship network, biological network, food chain network and etc.In the complex network, every node represent one entity in the network, and the edgerepresent the relationship between two entities. In the co-authorship network, nodesrepresent authors, while the edges represent co-authorships, that means the two
authors that linked by the edge co-author one paper in that network. More and moreresearches show that, although there are so many different types of complex networkthat may be refer to different subject areas, they have lots of the common statisticalproperty: small-world effect,clustering features, scale-free and so on.With the deep study of complex network in the physical and mathematical aspects,people find that many networks have the community property, that is to say, a network
is consisted by lots of communities, nodes in the community are more dense, whilethe connection between communities is rarer relatively(Wang et al., 2006 ). Usually,nodes in the same community have the same property. In the co-authorship network,scientists in one community always have the similar study interesting, and in the
WWW network, web page in the same community usually refer to the related topic.Finding the community structure in the network is very important for people to fullyunderstand the network structure and analyse the network properties.The community detection technology has already developed in recent years, GNalgorithm, W-H algorithm, hierarchical clustering algorithm and etc. are the inchoatealgorithms, however, with the passage of time, algorithms have been developed,algorithms have becoming more operable, that includes modified GN algorithm,Clique Percolation Method(CPM), Discovering Web Community algorithm and etc.
Evolving communities in networks

1.2Aims and objectives

1.3Appliance of complex network in the literature system

2.1 Complex Network
2.1.1 Basic concept

2.2 Communities
2.2.1 The community in sociology

2.2.2 The community in network

3.1 Community finding algorithms

3.2.1 K-clique community

3.2.2 Extraction of the k-clique-communities

4.2 Community evolution
4.2.1 Evolution path

4.2.2 The types of evolving communities

References
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