scispace - formally typeset
Search or ask a question
Author

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
More filters
Journal ArticleDOI
01 Jul 2006
TL;DR: In this paper, a papier mets en evidence quelqu'unes differences entre les journaux de "science normale" dans des disciplines telles que la physique et la chimie, and des "sciences non-normales" dins des disciplines de sciences sociales.
Abstract: Editer un journal de science normale dans les sciences sociales : Cet papier mets en evidence quelqu'unes des differences entre les journaux de "science normale" dans des disciplines telles que la physique et la chimie, et des "sciences non-normales" dans des disciplines de sciences sociales. L'article montre qu'un journal, Social Networks, ressemble plus a un journal de science normale qu'un journal typique des sciences sociales. L'auteur montre que les proprietes de sciences normales de la recherche sur des reseaux sociaux sont engendrees par l'utilisation des images graphiques et des modeles mathematiques, et la disponibilite d'ordinateurs capables d'analyser des ensembles de donnees a structures relativement complexes.

2 citations

Journal ArticleDOI
TL;DR: Dunbar's notion that the limit on an individual's information processing capacity imposes a limit on group size depends on how the group is conceived as discussed by the authors, and there is no necessary connection between the number of others with whom an individual has a personal tie and the sizes of the "groups" in the sense they were defined by Dunbar.
Abstract: Dunbar's basic idea that neocortical size constrains "the number of relationships an animal can keep track of in a complex, continually changing social world" is appealing, but the notion that such a limit leads to a constraint on the size of the social groups in which the individual is embedded is less so. The problem is in Dunbar's casual treatment of groups. Over 30 years ago Floyd Allport (1961, p. 195) pointed out that because "a group is a phenomenon so familiar to everyone that it is not a question of what 3 group is, but only of how it works," researchers had simply "assumed the existence of groups." This is precisely what Dunbar has done. Dunbar's notion that the limit on an individual's information processing capacity imposes a limit on group size depends on how the group is conceived. He defines a group as a subset of a population of conspecifics that "interacts on a sufficiently regular basis to have strong bonds based on direct personal knowledge." For their knowledge to be personal, each individual must interact "on a sufficiently regular basis" with each and every other individual in the group. A subset that is maximal with respect to that property has been formally dubbed a "chque" (Luce & Perry 1949). The properties of chques can be specified in exact terms. Given a finite collection of individuals A = (a,b,c, . . . ) along with a symmetric relation / that links those pairs of individuals in A that interact on a sufficiently regular basis to have "strong bonds," suppose that each individual in A has the relation / with n other individuals; n is then the number of others with whom an individual has a "personal" tie. Suppose further that we find a clique in A of size m. If Dunbar is right, there must be a relationship between n and m. But the value of m only sets a lower limit on n, n a m — 1. The upper limit of n depends on the arrangement of the ties linking individuals in A, and there is no necessary connection between the number of others with whom an individual has a personal tie and the sizes of the "groups" in the sense they were defined by Dunbar. Dunbar may, however, have had other (unstated) restrictions in mind when he talked about groups. In his groups, for example, he may have assumed that "friends of friends are fiiends." In that case, the relation / would be transitive and each group would be a special kind of clique that Davis (1967) called a cluster. All individuals within each cluster would be directly hnked, and no individuals falling in difiFerent clusters would be. In that case, n = m — 1, and individual network size would be inextricably tied to group size. But, at least in the case of human primates, interaction fi-equencies are certainly not transitive (Freeman 1992b). Humans do display some tendency to strain toward transitivity in

2 citations

Journal ArticleDOI
TL;DR: A computer controlled experiment is described designed to investigate the consequences of reinforcement of negative recency on its occurrence and the substance of Estes' pattern model of such learning is examined.
Abstract: Both theroretical work and research results have been somewhat equivocal on the subject of recency in the two-choice probability learning task. The present paper describes a computer controlled experiment designed to investigate the consequences of reinforcement of negative recency on its occurrence. Results are examined in terms of both the methodology of computer controlled experimentation and the substance of Estes' pattern model of such learning.

1 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a plan for making introductory statistics a tolerable, perhaps desirable, course in the liberal arts curriculum, based on the theory of the panic reaction.
Abstract: Everyone who has taught a course in introductory statistics to majors in sociology, psychology, education, and the like has been faced by the Great Panic Reaction. In another course the instructor can say, \"The occupational system is essentially the institutionalized differentiation of the adaptive aspect of the task-orientation area of the social system,\" l and be met with only a soft murmur of discontent. Yet, at the mere mention of the number \"three,\" student apprehension will rise to the panic level. Voicing an expression like \"sigma-ex\" will lead to a barrage of drop-slips and changes-in-major. Something in our cultural background seems to engender these anxieties; at some time in our formative years we are frightened by the magic of numbers. Genuine learning cannot take place in an atmosphere fraught with anxieties, and the fears must be allayed before students can attend to subject matter. And so the teacher-if he is to teach at all-must be concerned first with the fear and only secondly with the subject matter of statistics. He must be a religious confessor, a psychotherapist, and a numbers magician all rolled up into a neat package. It is not the intent of the present paper to explore the basis for the Panic Reaction. That is a problem for research in educational psychology and sociology. Rather, we hope to outline a device which may help to allay the anxiety. We are convinced that much of the fear engendered by a first experience with statistics may be eliminated by sound, systematic, and logical course organization. We shall present a plan-a course outlinedesigned to provide such an order. It is hoped that through the application of such a plan introductory statistics may become a tolerable, perhaps desirable, course in the liberal arts curriculum.

1 citations


Cited by
More filters
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