Large networks, having thousands of vertices and lines, can be found in many different areas, e. g: genealogies, flow graphs of programs, molecule, computer networks, transportation networks, social networks, intra/inter organisational networks ... Many standard network algorithms are very time and space consuming and therefore unsuitable for analysis of such networks. In the article we present some approaches to analysis and visualisation of large networks implemented in program Pajek. Some typical examples are also given. 1 Introduction Pajek (Slovene word for Spider) is a program, for Windows (32 bit), for analysis of large networks. It is freely available, for noncommercial use, at its homepage: http://vlado.fmf.uni-lj.si/pub/networks/pajek/ Large networks can be found in many different areas. Usually they are produced automatically, using computers, from different data sources that are already available in computer readable form. For example: large genealogies (genea...

Pajek is a program, for Windows, for analysis and visualization of large networks having some ten or houndred of thousands of vertices. In Slovenian language pajek means spider.

We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape's surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on data from the graph drawing literature.

In this article, we define webometrics within the framework of informetric studies and bibliometrics, as belonging to library and information science, and as associated with cybermetrics as a generic subfield. We develop a consistent and detailed link typology and terminology and make explicit the distinction among different Web node levels when using the proposed conceptual framework. As a consequence, we propose a novel diagram notation to fully appreciate and investigate link structures between Web nodes in webometric analyses. We warn against taking the analogy between citation analyses and link analyses too far.

Network evolution is a ubiquitous phenomenon in a wide variety of complex systems. There is an increasing interest in statistically modeling the evolution of complex networks such as small-world networks and scale-free networks. In this article, we address a practical issue concerning the visualization of network evolution. We compare the visualizations of co-citation networks of scientific publications derived by two widely known link reduction algorithms, namely minimum spanning trees (MSTs) and Pathfinder networks (PFNETs). Our primarily goal is to identify the strengths and weaknesses of the two methods in fulfilling the need for visualizing evolving networks. Two criteria are derived for assessing visualizations of evolving networks in terms of topological properties and dynamical properties. We examine the animated visualization models of the evolution of botulinum toxin research in terms of its co-citation structure across a 58-year span (1945-2002). The results suggest that although high-degree nodes dominate the structure of MST models, such structures can be inadequate in depicting the essence of how the network evolves because MST removes potentially significant links from high-order shortest paths. In contrast, PFNET models clearly demonstrate their superiority in maintaining the cohesiveness of some of the most pivotal paths, which in turn make the growth animation more predictable and interpretable. We suggest that the design of visualization and modeling tools for network evolution should take the cohesiveness of critical paths into account.

In the paper very efficient, linear in number of arcs, algorithms for determining Hummon and Doreian’s arc weights SPLC and SPNP in citation network are proposed, and some theoretical properties of these weights are presented. The nonacyclicity problem in citation networks is discussed. An approach to identify on the basis of arc weights an important small subnetwork is proposed and illustrated on the citation networks of SOM (self organizing maps) literature and US patents.

Abstract not found

Abstract not found

Knowledge diffusion is the adaptation of knowledge in a broad range of scientific and

A network can be defined as a set of linked nodes, where nodes rep-resent actors and links represent the relationships between actors. Net-works can be simple, such as a family structure, or very complex, such as the World Wide Web. Researchers have long explored network struc-tures; physicists, sociologists, statisticians and mathematicians have all contributed to the large body of literature that exists on the many facets of network analysis. Any network, simple or complex, that fosters social connections is a social network. As the web is a social network, how can we better understand the social analytic framework that influences current information retrieval and ranking techniques? To do so, we must examine the theoretical and methodological tradition of social network analysis. This paper attempts to provide this informative context. In section 1, we are introduced to the network analytic paradigm. In section 2, we ex-amine theoretical perspectives on social network analysis, exploring social power and social capital in detail. In section 3, we survey methodology in social network analysis, with special attention paid to the network epi-demiology. In section 4, we examine our current web analytic paradigm in the context of social network analysis. In section 5, conclusions and 1 further directions are presented. 1