Humans are the most social of all animals, learning from and being interdependent with many others, thereby forming relationships that span our complex world-society. Understanding humans and humanity therefore requires comprehending social relations, which may be hard to grasp systematically. Fortunately, the variety and turmoil of social relations can be mapped out as clear-cut networks. With the aid of network theory, characteristics of social life can be pointed out and explained that nobody imagined before, such as small worlds, highly skewed distributions of social contacts, and the structure of social inequality and cohesion. Furthermore, phenomena that are not yet well understood can be grasped more clearly, such as organizations and the growth and diffusion of knowledge. Many social network models are also applicable to, or inspired by, other fields, such as economics, biology, political science, statistical physics, and organization science.

This book introduces social networks to a general audience, from novices in all kinds of fields to experts wanting to catch up, and from academics on the one hand to practitioners in consultancy, management, policy, and social work on the other hand. Sophisticated models are lucidly explained and comprehensible without math (which is put in boxes, footnotes, or references), and are illustrated with network diagrams and examples ranging from anthropology to organizational sociology. A free to use software tool - R's igraph package - is explained in a manual on this website so readers themselves can depict and analyze networks of interest to them.

Table of contents, and sheets (updated shortly before the pertaining lecture)
   1. Introduction
   2. Representation and conceptualization
   2. Small worlds
   4. Searching and fat tails
   5. Communities: detection, conflict, cohesion, and culture
   6. Social inequality: prestige, power, brokerage and roles
       Only on the Web: Bonacich' power centrality
   7. Organizations as networks
   8. Methods: data and software. See the computer manual that updates and replaces the software part of Ch.8.
       Glossary

Courses
Summer School Saint Petersburg Sheets

(Autumn 2011) You can follow the links to the course syllabus as well as to the assignments, pertaining data sets, and a computer manual. Sheets of lectures are uploaded shortly before the pertaining lecture.

Updates after the manuscript was completed (early 2008).
   1. The software has been updated to the point where the treatment of it in the book has become partly obsolete, and is replaced by this manual.
   2. Community detection is now also possible for networks with both positive and negative ties, and is forthcoming in igraph 0.6. That version also has its index set harmonized--no longer necessary to reset the index from n to n-1. For Linux, a development version of igraph is available, and can be installed by the command  R CMD INSTALL --build igraph_0.6.tar.gz  For Windows I have no idea how to install it; all good advice on the Web seems not to apply to my Windows computer, which only becomes angry when I try.
   3. For statistical models of network change, see Tom Snijders' SIENA site. The R package for it is RSiena.
   4. Data can be found at the SIENA site (mentioned above), as well as Wikipedia, Casos, Networks Workbench, Stanford, Enron's email traffic, Mark Newman, Fowler's legislative cosponsorship, and international trade. They can sometimes be related to node data, e.g. for international trade there are country data. In the Netherlands there is DANS with many data, a.o. on networks. On how to use Google to collect network data, see Lee et al. (2010).
   5. As strategic information for brokers seems to be limited to direct contacts (Burt 2010), for betweenness centrality you should leave out paths longer than 2 by using the command > betweenness.estimate(graph, cutoff=2)
   7. It has become even more clear that for ties to contribute to social cohesion they have to be bi-directional (Grannis 2009); if they are, they can be represented as edges, just like in the book. However, K-connectivity is computationally very complex and can only be computed for small networks. A computationally efficient alternative is k-core (Seidman 1983), which is at the same time a (coarse grained) centrality measure of how influential individuals are (Kitsak et al 2010; Holhoefer, Rivero, and Moreno 2011). It's shortcoming is that it does not perceive topological bottlenecks.
   8. People (in the USA) do not know on average about 750 people, as was thought in 2006, but about 600 (McCormick, Salganik and Zheng 2010).
   9. On assessing degree distributions, that are power law with exponential cut-offs, see Minnhagen.

Errata
A network component is a set of nodes where each node can be reached from any other through a path (a concatenation of lines), as said in the book. However, a component is also maximal in the sense that every node that can be reached belongs to the component in question. For example, organizations are components for the authority relation, at least for a period of the day (as members can work for other organizations at other times.)

Images
The printer of the book ruined my pictures by turning them into a grayish fog, for which I'm not responsible.