Applications of complex network science

Pao Yue-kong Library Electronic Theses Database

Applications of complex network science

 

Author: Liu, Xiaofan
Title: Applications of complex network science
Degree: Ph.D.
Year: 2012
Subject: Network analysis (Planning)
Computer networks.
Social networks.
Social sciences -- Network analysis.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Electronic and Information Engineering
Pages: xviii, 163 p. : ill. (some col.) ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2551318
URI: http://theses.lib.polyu.edu.hk/handle/200/6827
Abstract: Systems of inter-connected components can be modeled by networks. A network is a collection of "nodes" linked by "edges". The network models of large scale complex systems, e.g., social networks, transportation systems, engineering infrastructures, etc., have shown some non-trivial universal structural properties compared to regular graphs such as lattices or random graphs. These network models are hence referred to as complex networks. In this thesis the author analyzes the structure and dynamics of the complex networks abstracted from different systems, including arts, finance and social systems, in order to describe the functioning mechanisms and to optimize the functionalities of the systems. In Chapter 3, music compositions are analyzed from a complex network perspective. By abstracting music notes to network nodes and the co-occurrence of notes to edges between nodes, it is shown that different genres of music, ranging from pop to classic, western to oriental, all display universal network properties. It is shown that the theme of music is also embedded in the underlying complex networks of music compositions. Computer algorithms for automatic music generation are proposed based on a random walk process in the musical networks. Applying some additional rudimentary rhythmic constraints on the computer algorithms, appealing music can be generated.
In Chapter 4, global stock markets are analyzed from a complex network perspective. The dynamical complex network model considers each stock market as a node and connects each pair of nodes by an edge. Each edge is assigned by a time varying weight which equals the dynamic conditional correlation of the market index return values. The network properties reveal that the global stock market integration exhibits a periodic behavior, and that developed markets show stronger integration than emerging and frontier markets. The network also exhibits strong synchronization when the markets experience volatility. Unlike in the market integration, this volatility spillover phenomenon is more obvious in frontier markets than in developed markets. In Chapter 5, scientific collaborations are analyzed from a complex network perspective. With scientists abstracted by network nodes and the coauthorship of academic papers by edges between nodes, the collaboration network has been found to be intensively evolving in a local scale, where most coauthorships are one time collaboration. The structure of the collaboration network of a scientist depicts his/her influence in the peer collaborators. By examining the impact of unforeseen deaths of active eminent scientists to their collaboration networks, it is found that the fast evolving scientific collaboration networks are very robust to attacks on the hub nodes. It is suggested that future design of engineering systems should imitate the evolution mechanism of human social interactions to create robust systems. In Chapter 5, the consensus process in online social networks is analyzed from a complex network perspective. Users of online social websites form social communities by indicating friendship or interacting with each other. The resulting networks of online social relationship can be considered snapshots of the entire human social communities. By studying the consensus process in an online social network, it is found that a few users with very large number of connections can shift the final consensus decision of the community. Moreover,the compartmental structure of social networks can actually slow down the consensus rate.

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