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Katherine Faust

Bio: Katherine Faust is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Social network & Social network analysis (criminology). The author has an hindex of 29, co-authored 58 publications receiving 34066 citations. Previous affiliations of Katherine Faust include University of South Carolina & University of California.


Papers
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Journal ArticleDOI
TL;DR: Scaling and statistical models are used to study networks of ties among Soviet politicians during the Brezhnev era created by their co-attendance at events and reveal that participation is patterned by the state and party offices elites hold.

61 citations

Journal ArticleDOI
TL;DR: A unique dataset from Nang Rong, Thailand which contains dwelling unit locations (GPS) and saturated kinship networks of all individuals living in 51 agricultural villages is used and shows that in general, extended kin live closer to one another than do unrelated individuals.

59 citations

Book ChapterDOI
01 Feb 2005
TL;DR: In this paper, the authors describe and illustrate methods for studying affiliation networks, with special attention to methods for spatial representations that jointly display the actors and events in the network, and describe the formal relationships embodied in the configuration and explicit description of how the result corresponds to the original data.
Abstract: This chapter describes and illustrates methods for studying affiliation networks, with special attention to methods for spatial representations that jointly display the actors and events in the network. Although affiliation networks have been the focus of methodological research for decades (Levine 1972; Breiger 1974; Seidman 1981; McPherson 1982; Wilson 1982), more recent analyses of affiliation networks have raised a number of issues concerning appropriate methods for their study. At the same time, research has pointed to the empirical and theoretical generality of this perspective (Freeman and White 1993; Wasserman and Faust 1994; Borgatti and Everett 1997; Faust 1997; Skvoretz and Faust 1999; Breiger 2000; Mische and Pattison 2000; Roberts 2000; Brazill and Groffman 2002; Faust et al. 2002; Pattison and Breiger 2002). Background Representing the two modes in the affiliation network in a “joint space” in which both actors and events are depicted simultaneously is of particular interest in both earlier and more recent work on affiliation networks. Such graphic displays commonly use scaling (e.g., correspondence analysis) or algebraic approaches (e.g., lattices). An important, but often neglected, aspect of some applications is clear specification of the formal relationships embodied in the configuration and explicit description of how the result corresponds to the original data. These omissions produce rather casual depictions and consequent ambiguity in interpretation. They also contribute to misunderstanding and fuel debate about the usefulness of the approach. The following passages are typical of such descriptions for affiliation networks or similar two-mode data arrays.

57 citations

Journal ArticleDOI
TL;DR: It is shown that use of distance as a measure of similarity without proper attention to appropriate standardization procedures confounds information on differences between means and differences between variances with information on the similarity of the patterns between pairs of individuals.

53 citations

Journal ArticleDOI
TL;DR: In this article, a considerable literature has emerged within criminology stemming from the collection of social network data and the adoption of Social network analysis by a cadre of scholars, including the authors of this paper.
Abstract: Over the past decade, a considerable literature has emerged within criminology stemming from the collection of social network data and the adoption of social network analysis by a cadre of scholars...

49 citations


Cited by
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Journal ArticleDOI
04 Jun 1998-Nature
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

39,297 citations

Book
08 Sep 2000
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

23,600 citations

Journal ArticleDOI
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Abstract: The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

18,415 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a model that incorporates this overall argument in the form of a series of hypothesized relationships between different dimensions of social capital and the main mechanisms and proces.
Abstract: Scholars of the theory of the firm have begun to emphasize the sources and conditions of what has been described as “the organizational advantage,” rather than focus on the causes and consequences of market failure. Typically, researchers see such organizational advantage as accruing from the particular capabilities organizations have for creating and sharing knowledge. In this article we seek to contribute to this body of work by developing the following arguments: (1) social capital facilitates the creation of new intellectual capital; (2) organizations, as institutional settings, are conducive to the development of high levels of social capital; and (3) it is because of their more dense social capital that firms, within certain limits, have an advantage over markets in creating and sharing intellectual capital. We present a model that incorporates this overall argument in the form of a series of hypothesized relationships between different dimensions of social capital and the main mechanisms and proces...

15,365 citations

Journal ArticleDOI
TL;DR: This article reviews studies investigating complex brain networks in diverse experimental modalities and provides an accessible introduction to the basic principles of graph theory and highlights the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
Abstract: Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

9,700 citations