Entity Resolution on Complex Network

Entity Resolution on Complex Network

Copyright: © 2014 |Pages: 27
DOI: 10.4018/978-1-4666-5198-2.ch009
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Complex networks can be used to describe the Internet, social network, or more broadly describe a binary relation of a set of objects. Structure information of complex network helps the identification of the entity corresponding to nodes in the network. There is much research in this area, and the authors introduce these studies and their results in this chapter. The authors mainly present two practical applications as an example. Through these examples, the authors explore the research ideas in entity resolution on complex network. The applications of entity resolution on complex network include the detection of mirror Websites, name recognition in social network, and information searching on the Internet. This chapter introduces some applications, including the detection of mirror Websites and name recognition, in social network in detail.
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The study of complex networks is a young and active area of scientific research inspired largely by the empirical study of real-world network such as computer network and social network. In recent years, the academic study of complex network is in the ascendant. In particular, two international pioneering works set off a boom of complex network's study. Watts & Strogatz (1998) introduces a small-world network model to describe a conversion from completely regular network to a completely random network. Small-world network not only has clustering characteristics similar to the regular network, but also has a smaller average path length similar to the random network. Barabási & Albert (1999) pointed out that many practical connectivities on complex network spread in a power-law form. .

With no obvious characteristic length, such networks are called scale-free network. Then, scientists have studied the various characteristics of a variety of complex network.

Domestic scholars have noticed this trend, and began to expand research. The scholars who join the study of complex network are mainly from the field of graph theory, statistical physics, computer network research, ecology, sociology and economics. The network involved in the research included all kinds of network in the field of life sciences (such as cellular networks, protein - protein interaction networks, protein folding networks, neural networks, ecological networks), Internet / WWW network, social networks, network of epidemic spread of the disease, scientists cooperation network, the network of human sexual relations, linguistics network, and so on. The primary methods include graph theory, statistical physics and social network analysis.

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