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Linton C. Freeman

Other affiliations: Lehigh University, Syracuse University, University of California  ...read more
Bio: Linton C. Freeman is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Centrality & Social network. The author has an hindex of 38, co-authored 82 publications receiving 27411 citations. Previous affiliations of Linton C. Freeman include Lehigh University & Syracuse University.


Papers
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Journal ArticleDOI
TL;DR: In this article, three distinct intuitive notions of centrality are uncovered and existing measures are refined to embody these conceptions, and the implications of these measures for the experimental study of small groups are examined.

14,757 citations

Journal ArticleDOI
01 Mar 1977
TL;DR: A family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced in this paper, which define centrality in terms of the degree to which a point falls on the shortest path between others and there fore has a potential for control of communication.
Abstract: A family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced. These measures define centrality in terms of the degree to which a point falls on the shortest path between others and there fore has a potential for control of communication. They may be used to index centrality in any large or small network of symmetrical relations, whether connected or unconnected.

8,026 citations

Book
23 Jul 2004
TL;DR: In this paper, the authors describe the process of centrifugally casting an article such as a tire or the like from a curable or hardenable liquid polymeric material, which process includes the steps of selecting a mold and placing a core within the mold which core is hollow and/or is readily deformable under pressure but which has sufficient memory to resume its original position when the pressure is removed.
Abstract: The process of centrifugally casting an article such as a tire or the like from a curable or hardenable liquid polymeric material, which process includes the steps of selecting a mold and placing a core within the mold which core is hollow and/or is readily deformable under pressure but which has sufficient memory to resume its original position when the pressure is removed. The article being formed is formed between the core and the mold. The space between the core and the mold is filled with the curable liquid material of which the article is to be formed and the hollow core is also filled with a liquid material. The liquid material in the hollow core, the material from which the core is constructed and the curable liquid material all have about the same specific gravity. The mold and core are rotated to centrifugally cast the article which is formed of the curable liquid material. The deformable core permits easy removal from the completed article and the matching of the specific gravities as aforementioned keeps the deformable core from distorting during the centrifugal casting operation. The order of introducing the liquid material into the core, introducing the curable liquid into the space between the mold and core and rotation of the mold and core can be varied within the limits as set forth in the following description. Variations in the structure for accomplishing the principle of matching specific gravities are illustrated and described.

1,098 citations

Journal ArticleDOI
TL;DR: A new measure of centrality, C, is introduced, based on the concept of network flows, which is defined for both valued and non-valued graphs and applicable to a wider variety of network datasets.

996 citations

Journal Article
TL;DR: The historian Alfred Crosby (1997) has proposed that visualization is one of only two factors that are responsible for the explosive development of all of modern science.
Abstract: The use of visual images is common in many branches of science. And reviewers often suggest that such images are important for progress in the various fields (Koestler, 1964; Arnheim, 1970; Taylor, 1971; Tukey, 1972; Klovdahl, 1981; Tufte, 1983; Belien and Leenders). The historian Alfred Crosby (1997) has gone much further. He has proposed that visualization is one of only two factors that are responsible for the explosive development of all of modern science. The other is measurement.

727 citations


Cited by
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Journal ArticleDOI
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Abstract: 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 small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

17,647 citations

Journal ArticleDOI
TL;DR: In this article, three distinct intuitive notions of centrality are uncovered and existing measures are refined to embody these conceptions, and the implications of these measures for the experimental study of small groups are examined.

14,757 citations

Journal ArticleDOI
TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Abstract: A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known—a collaboration network and a food web—and find that it detects significant and informative community divisions in both cases.

14,429 citations

Journal ArticleDOI
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Abstract: We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.

12,882 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