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Book ChapterDOI

The social forces.

01 Jan 1897-pp 116-124
TL;DR: This paper present an attractive little volume of two hundred and twenty-six pages, neatly bound and printed upon excellent paper with wide margins and clear type, made up of twenty-five editorials appearing in The Survey in 1907 and 1908.
Abstract: This is an attractive little volume of two hundred and twenty-six pages, neatly bound and printed upon excellent paper with wide margins and clear type. It is a literary mosaic made up of twenty-five editorials appearing in The Survey in 1907 and 1908. These more or less disconnected bits of expression upon social phenomena in general are put together with so much skill and delicacy of workmanship that they present a symmetrical and well-balanced whole. That which gives them unity and continuity is the large sociological experience and economic grasp of the writer. This appears, for example, in the constantly reiterated principles, that charities should deal primarily with social causes, and that relief should not only be preventive of pauperism but should
Citations
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Journal ArticleDOI
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.

9,441 citations

Journal ArticleDOI
Mark Newman1
TL;DR: It is shown that these collaboration networks form "small worlds," in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances.
Abstract: The structure of scientific collaboration networks is investigated. Two scientists are considered connected if they have authored a paper together and explicit networks of such connections are constructed by using data drawn from a number of databases, including MEDLINE (biomedical research), the Los Alamos e-Print Archive (physics), and NCSTRL (computer science). I show that these collaboration networks form “small worlds,” in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. I further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied.

4,529 citations

Journal ArticleDOI
17 May 2002-Science
TL;DR: A model is presented that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions that may be applicable to many network search problems.
Abstract: Social networks have the surprising property of being "searchable": Ordinary people are capable of directing messages through their network of acquaintances to reach a specific but distant target person in only a few steps. We present a model that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions. Our model defines a class of searchable networks and a method for searching them that may be applicable to many network search problems, including the location of data files in peer-to-peer networks, pages on the World Wide Web, and information in distributed databases.

1,015 citations

Journal ArticleDOI
TL;DR: A measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar is proposed, which leads to a self-consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network.
Abstract: We consider methods for quantifying the similarity of vertices in networks. We propose a measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar. This leads to a self-consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network. We test our similarity measure on computer-generated networks for which the expected results are known, and on a number of real-world networks.

844 citations


Cites background from "The social forces."

  • ...(Burt, 1976) and (Goldberg and Roth, 2003) propose measures involving √ σunnorm andσ 2 unnorm, respectively.)...

    [...]

Journal ArticleDOI
TL;DR: A diversity of phenomena are surveyed, which may be classified into no less than 11 areas, providing a clear indication of the impact of the field of complex networks.
Abstract: The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches and in applications to real-world problems. Complex networks have fared extremely well in both of these aspects, with their sound theoretical basis developed over the years and with a variety of applications. In this survey, we analyze the applications of complex networks to real-world problems and data, with emphasis in representation, analysis and modeling, after an introduction to the main concepts and models. A diversity of phenomena are surveyed, which may be classified into no less than 22 areas, providing a clear indication of the impact of the field of complex networks.

615 citations

References
More filters
Journal ArticleDOI
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.

9,441 citations

Journal ArticleDOI
Mark Newman1
TL;DR: It is shown that these collaboration networks form "small worlds," in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances.
Abstract: The structure of scientific collaboration networks is investigated. Two scientists are considered connected if they have authored a paper together and explicit networks of such connections are constructed by using data drawn from a number of databases, including MEDLINE (biomedical research), the Los Alamos e-Print Archive (physics), and NCSTRL (computer science). I show that these collaboration networks form “small worlds,” in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. I further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied.

4,529 citations

Journal ArticleDOI
17 May 2002-Science
TL;DR: A model is presented that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions that may be applicable to many network search problems.
Abstract: Social networks have the surprising property of being "searchable": Ordinary people are capable of directing messages through their network of acquaintances to reach a specific but distant target person in only a few steps. We present a model that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions. Our model defines a class of searchable networks and a method for searching them that may be applicable to many network search problems, including the location of data files in peer-to-peer networks, pages on the World Wide Web, and information in distributed databases.

1,015 citations

Journal ArticleDOI
TL;DR: A measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar is proposed, which leads to a self-consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network.
Abstract: We consider methods for quantifying the similarity of vertices in networks. We propose a measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar. This leads to a self-consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network. We test our similarity measure on computer-generated networks for which the expected results are known, and on a number of real-world networks.

844 citations

Journal ArticleDOI
TL;DR: A diversity of phenomena are surveyed, which may be classified into no less than 11 areas, providing a clear indication of the impact of the field of complex networks.
Abstract: The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches and in applications to real-world problems. Complex networks have fared extremely well in both of these aspects, with their sound theoretical basis developed over the years and with a variety of applications. In this survey, we analyze the applications of complex networks to real-world problems and data, with emphasis in representation, analysis and modeling, after an introduction to the main concepts and models. A diversity of phenomena are surveyed, which may be classified into no less than 22 areas, providing a clear indication of the impact of the field of complex networks.

615 citations