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36
The structure and function of complex networks
 SIAM REVIEW
, 2003
"... Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, ..."
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Cited by 1467 (9 self)
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Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the smallworld effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Characterization of complex networks: A survey of measurements
 ADVANCES IN PHYSICS
, 2005
"... Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics and function of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of mea ..."
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Cited by 94 (7 self)
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Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics and function of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements organized into classes. Special attention is given to relating complex network analysis with the areas of pattern recognition and feature selection, as well as on surveying some concepts and measurements from traditional graph theory which are potentially useful for complex network research. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the
A General Model of Web Graphs
, 2003
"... We describe a very general model of a random graph process whose proportional degree sequence obeys a power law. Such laws have recently been observed in graphs associated with the world wide web. ..."
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Cited by 83 (7 self)
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We describe a very general model of a random graph process whose proportional degree sequence obeys a power law. Such laws have recently been observed in graphs associated with the world wide web.
A Random Graph Model for Power Law Graphs
 Experimental Math
, 2000
"... We propose a random graph m del which is a special case of sparse random graphs with given degree sequences which satisfy a power law. Thism odel involves only asm all num ber of param eters, called logsize and loglog growth rate. These param eters capturesom e universal characteristics ofm assive ..."
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Cited by 73 (4 self)
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We propose a random graph m del which is a special case of sparse random graphs with given degree sequences which satisfy a power law. Thism odel involves only asm all num ber of param eters, called logsize and loglog growth rate. These param eters capturesom e universal characteristics ofm assive graphs. Furtherm re, from these paramfi ters, various properties of the graph can be derived. Forexam)(( for certain ranges of the paramJ?0CM we willcom?C7 the expected distribution of the sizes of the connectedcom onents which almJC surely occur with high probability. We will illustrate the consistency of our m del with the behavior of so m m ssive graphs derived from data in telecom unications. We will also discuss the threshold function, the giant com ponent, and the evolution of random graphs in thism del. 1
A Geometric Preferential Attachment Model of Networks
 In Algorithms and Models for the WebGraph: Third International Workshop, WAW 2004
, 2004
"... We study a random graph Gn that combines certain aspects of geometric random graphs and preferential attachment graphs. This model yields a graph with powerlaw degree distribution where the expansion property depends on a tunable parameter of the model. The vertices of Gn are n sequentially generat ..."
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Cited by 32 (2 self)
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We study a random graph Gn that combines certain aspects of geometric random graphs and preferential attachment graphs. This model yields a graph with powerlaw degree distribution where the expansion property depends on a tunable parameter of the model. The vertices of Gn are n sequentially generated points x1, x2,..., xn chosen uniformly at random from the unit sphere in R 3. After generating xt, we randomly connect it to m points from those points in x1, x2,..., xt−1. 1
Recommender systems research: a connectioncentric survey
 J. INTELL. INF. SYST
, 2004
"... Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and ..."
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Cited by 31 (2 self)
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Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a contentbased filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is underemphasized in the recommender systems literature. We therefore take a connectionoriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connectioncentric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.
Storageclass memory: The next storage system technology
 IBM JOURNAL OF RESEARCH AND DEVELOPMENT
, 2008
"... The dream of replacing rotating mechanical storage, the disk drive, with solidstate, nonvolatile RAM may become a reality in the near future. Approximately ten new technologies—collectively called storageclass memory (SCM)—are currently under development and promise to be fast, inexpensive, and po ..."
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Cited by 23 (0 self)
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The dream of replacing rotating mechanical storage, the disk drive, with solidstate, nonvolatile RAM may become a reality in the near future. Approximately ten new technologies—collectively called storageclass memory (SCM)—are currently under development and promise to be fast, inexpensive, and power efficient. Using SCM as a disk drive replacement, storage system products will have random and sequential I/O performance that is orders of magnitude better than that of comparable diskbased systems and require much less space and power in the data center. In this paper, we extrapolate disk and SCM technology trends to 2020 and analyze the impact on storage systems. The result is a 100 to 1,000fold advantage for SCM in terms of the data center space and power required.
Random Deletion In A Scale Free Random Graph Process
, 2004
"... We study a dynamically evolving random graph which adds vertices and edges using preferential attachment and deletes vertices randomly. At time t, with probability #1 > 0 we add a new vertex u t and m random edges incident with u t . The neighbours of u t are chosen with probability proportional ..."
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Cited by 16 (3 self)
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We study a dynamically evolving random graph which adds vertices and edges using preferential attachment and deletes vertices randomly. At time t, with probability #1 > 0 we add a new vertex u t and m random edges incident with u t . The neighbours of u t are chosen with probability proportional to degree. With probability # 0 we add m random edges to existing vertices where the endpoints are chosen with probability proportional to degree. With probability 1 #0 we delete a random vertex, if there are vertices left to delete. and with probability #0 we delete m random edges. Assuming that #+#1 +#0 > 1 and #0 is su#cently small, we show that for large k, t, the expected number of vertices of degree k is approximately dk t where as k ##, dk Ck 1# where # = 2(## 0 ) 3#1# 1 and C > 0 is a constant. Note that # can take any value greater than 1. 1
Mining market data: A network approach
, 2005
"... We consider a network representation of the stock market data referred to as the market graph, which is constructed by calculating crosscorrelations between pairs of stocks based on the opening prices data over a certain period of time. We study the evolution of the structural properties of the mar ..."
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Cited by 16 (1 self)
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We consider a network representation of the stock market data referred to as the market graph, which is constructed by calculating crosscorrelations between pairs of stocks based on the opening prices data over a certain period of time. We study the evolution of the structural properties of the market graph over time and draw conclusions regarding the dynamics of the stock market development based on the interpretation of the obtained results.
Statistical Analysis of Financial Networks
, 2005
"... Massive datasets arise in a broad spectrum of scientific, engineering and commercial applications. In many practically important cases, a massive dataset can be represented as a very large graph with certain attributes associated with its vertices and edges. Studying the structure of this graph is e ..."
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Cited by 13 (0 self)
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Massive datasets arise in a broad spectrum of scientific, engineering and commercial applications. In many practically important cases, a massive dataset can be represented as a very large graph with certain attributes associated with its vertices and edges. Studying the structure of this graph is essential for understanding the structural properties of the application it represents. Wellknown examples of applying this approach are the Internet graph, the Web graph, and the Call graph. It turns out that the degree distributions of al these graphs can be described by the powerlaw model. Here we consider another important application  a network representation of the stock market. Stock markets generate huge amounts of data, which can be used for constructing the market graph reflecting the market behavior. We conduct the statistical analysis of this graph and show that it also folliws the powerlaw model. Moreover, we detect cliques and independent sets in this graph. These special formations have a clear practical interpretation, and their analysis allows one to apply a new data mining technique of classifying financial instruments based on stock prices data, which provides a deeper insight into the internal structure of the stock market.