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.

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The Structure and Function of Complex Networks

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...

https://www.jstor.org/stable/pdfplus/259373.pdf

Social Capital, Intellectual Capital, and the Organizational Advantage

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.

/pdf/community-structure-in-social-and-biological-networks-1jamu4rcfv.pdf

Community structure in social and biological networks

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.

/pdf/finding-and-evaluating-community-structure-in-networks-1o9f6y8pyr.pdf

Finding and evaluating community structure in networks.

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

Networks and Relations The Development of Social Network Analysis Handling Relational Data Lines, Direction and Density Centrality and Centralization Components, Cores, and Cliques Positions, Roles, and Clusters Dimensions and Displays Appendix Social Network Packages

Social Network Analysis: A Handbook

This paper critically examines Gustavsen's argument that the extent of interlocking in company directorships is dependent on the level of organization in the corporate environment. Gustavsen's analysis of Nor way and Sweden is compared with time-series data for Scotland from 1906 to 1973. The trend in the figures seems to contradict Gustavsen's argu ment, since the overall level of interlocking has decreased whilst the complexity of the environment has increased. It is argued, however, that interlocking is not the only index of corporate interconnection and that a full analysis must also take account of trends in company mergers and acquisitions.

Trends in Interlocking Directorships: An International Comparison:

Social Network Analysis has been receiving more attention in Britain in recent years, but applications have been limited by the lack of availability of suitable computer programs for network analysis. Two packages are now readily available for use on Personal Computers, both coming 'with extensive manuals that explain the background of social network analysis as well as detailing how to use the programs. GRADAP has been converted from its original mainframe version to a PC version, and implements the major procedures of graph theory. STRUCTURE is specifically designed for the PC; and while it includes a more restricted range of measures, it goes beyond the confines of graph theory. GRADAP was developed by a group of Dutch social scientists interested in network analysis, and it was originally available only on certain CDC Cyber computers. The team has been working on a PC implementation for some years now, and Release 2.0 became available in 1988 (Release 1.0 was the mainframe version). The program is supplied on five disks, but must be installed on a hard disk on a computer with at least 512K of free memory. It claims to run on any IBM compatible computer, but a mathematical coprocessor must be fitted. Installation is very easy.' It is simply

Methods of Network Analysis

This paper presents a conceptual model of the business system in terms of its two constituent parts: the exchange network and the communication network. This model is applied to the Scottish econom...

John Scott

Papers

Social Network Analysis: A Handbook

Trends in Interlocking Directorships: An International Comparison:

Methods of Network Analysis

Capital and Communication in Scottish Business

The Structure of Sociological Theory and Knowledge