Vladimir Batagelj

Exploratory Social Network Analysis with Pajek: List of Illustrations

This is an extensively revised and expanded second edition of the successful textbook on social network analysis integrating theory, applications, and network analysis using Pajek. The main structural concepts and their applications in social research are introduced with exercises. Pajek software and data sets are available so readers can learn network analysis through application and case studies. Readers will have the knowledge, skill, and tools to apply social network analysis across the social sciences, from anthropology and sociology to business administration and history. This second edition has a new chapter on random network models, for example, scale-free and small-world networks and Monte Carlo simulation; discussion of multiple relations, islands, and matrix multiplication; new structural indices such as eigenvector centrality, degree distribution, and clustering coefficients; new visualization options that include circular layout for partitions and drawing a network geographically as a 3D surface; and using Unicode labels. This new edition also includes instructions on exporting data from Pajek to R software. It offers updated descriptions and screen shots for working with Pajek (version 2.03).

Comparing Resemblance Measures

In the paper some types of equivalences over resemblance measures and some basic results about them are given. Based on induced partial orderings on the set of unordered pairs of units a dissimilarity between two resemblance measures over finite sets of units can be defined. As an example, using this dissimilarity standard association coefficients between binary vectors are compared both theoretically and computationally.

Generalized blockmodeling of two-mode network data

Abstract We extend the direct approach for blockmodeling one-mode data to two-mode data. The key idea in this development is that the rows and columns are partitioned simultaneously but in different ways. Many (but not all) of the generalized block types can be mobilized in blockmodeling two-mode network data. These methods were applied to some ‘voting’ data from the 2000–2001 term of the Supreme Court and to the classic Deep South data on women attending events. The obtained partitions are easy to interpret and compelling. The insight that rows and columns can be partitioned in different ways can be applied also to one-mode data. This is illustrated by a partition of a journal-to-journal citation network where journals are viewed simultaneously as both producers and consumers of scientific knowledge.

Direct and indirect methods for structural equivalence

Abstract Procedures for establishing a partition of a network in terms of structural equivalence can be divided into direct and indirect approaches. For the former, a new criterion function is proposed that reflects directly structural equivalence concerns. This criterion function can then be (locally) optimized to create a partition. For indirect approaches, measures of dissimilarity must be compatible with the definition of structural equivalence.

Fast algorithms for determining (generalized) core groups in social networks

The structure of a large network (graph) can often be revealed by partitioning it into smaller and possibly more dense sub-networks that are easier to handle. One of such decompositions is based on “k-cores”, proposed in 1983 by Seidman. Together with connectivity components, cores are one among few concepts that provide efficient decompositions of large graphs and networks. In this paper we propose an efficient algorithm for determining the cores decomposition of a given network with complexity

A subquadratic triad census algorithm for large sparse networks with small maximum degree

{\mathcal{O}(m)}

Direct multicriteria clustering algorithms

, where m is the number of lines (edges or arcs). In the second part of the paper the classical concept of k-core is generalized in a way that uses a vertex property function instead of degree of a vertex. For local monotone vertex property functions the corresponding generalized cores can be determined in