Linton C. Freeman

Bio: Linton C. Freeman is an academic researcher from University of California, Irvine. The author has contributed to research in topic(s): Centrality & Social network. The author has an hindex of 38, co-authored 82 publication(s) receiving 27411 citation(s). Previous affiliations of Linton C. Freeman include Lehigh University & Syracuse University.

Centrality in social networks conceptual clarification

Abstract: The intuitive background for measures of structural centrality in social networks is reviewed and existing measures are evaluated in terms of their consistency with intuitions and their interpretability. Three distinct intuitive conceptions of centrality are uncovered and existing measures are refined to embody these conceptions. Three measures are developed for each concept, one absolute and one relative measure of the centrality of positions in a network, and one reflecting the degree of centralization of the entire network. The implications of these measures for the experimental study of small groups is examined.

A Set of Measures of Centrality Based on Betweenness

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.

The Development of Social Network Analysis: A Study in the Sociology of Science

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.

Centrality in valued graphs: A measure of betweenness based on network flow

Abstract: A new measure of centrality, C,, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman’s original measure, Ca, the new measure differs from the original in two important ways. First, C, is defined for both valued and non-valued graphs. This makes C, applicable to a wider variety of network datasets. Second, the computation of C, is not based on geodesic paths as is C, but on all the independent paths between all pairs of points in the network.

Visualizing Social Networks.

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.

The Structure and Function of Complex 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.

Centrality in social networks conceptual clarification

Abstract: The intuitive background for measures of structural centrality in social networks is reviewed and existing measures are evaluated in terms of their consistency with intuitions and their interpretability. Three distinct intuitive conceptions of centrality are uncovered and existing measures are refined to embody these conceptions. Three measures are developed for each concept, one absolute and one relative measure of the centrality of positions in a network, and one reflecting the degree of centralization of the entire network. The implications of these measures for the experimental study of small groups is examined.

Community structure in social and biological networks

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.

Finding and evaluating community structure in networks.

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.

Complex networks: Structure and dynamics

Abstract: Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks’ dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex wiring topology can behave collectively. We review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering. © 2005 Elsevier B.V. All rights reserved.