The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authoritative ” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages ” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristics for link-based analysis.

Methods for ranking World Wide Web resources according to their position in the link structure of the Web are receiving considerable attention, because they provide the first e#ective means for search engines to cope with the explosive growth and diversification of the Web. Closely related methods have been used in other disciplines for quite some time.

Background: The elucidation of whole-cell regulatory, metabolic, interaction and other biological networks generates the need for a meaningful ranking of network elements. Centrality analysis ranks network elements according to their importance within the network structure and different centrality measures focus on different importance concepts. Central elements of biological networks have been found to be, for example, essential for viability. Results: CentiBiN (Centralities in Biological Networks) is a tool for the computation and exploration of centralities in biological networks such as protein-protein interaction networks. It computes 17 different centralities for directed or undirected networks, ranging from local measures, that is, measures that only consider the direct neighbourhood of a network element, to global measures. CentiBiN supports the exploration of the centrality distribution by visualising central elements within the network and provides several layout mechanisms for the automatic generation of graphical representations of a network. It supports different input formats, especially for biological networks, and the export of the computed centralities to other tools. Conclusions: CentiBiN helps systems biology researchers to identify crucial elements of biological networks. CentiBiN including a user guide and example data sets is available free of charge at

An important characteristic of journals is how influential they are in the generation and dissemination of scholarly knowledge in a discipline. We report a citation analysis of 49 marketing and marketing-related journals to assess their relative influence based on the index of structural influence proposed by Salancik (1986). We investigate the level and span of influence of the 49 journals, both in the marketing discipline as a whole and in five specific sub-areas of marketing. As expected, the Journal of Marketing emerges as the most influential journal in the discipline and as the journal with the broadest span of influence across all sub-areas of marketing. However, different journals are most influential in each of the sub-areas, and the Journal of Marketing is particularly influential among the applied marketing journals. We also find that the index of structural influence is significantly correlated with all other measures of influence but least so with the impact factors report...

This paper identifies an important paradigm shift in social research on HIV transmission, drug abuse, and risk reduction research. The article describes the key research trends and the institutional support for social network analysis in the HIV and drug risk field for the past decade. Key hypotheses and recommended areas for future research are identified.

Techniques is a regular column devoted to techniques of data construction, management, interpretation and analysis. Contributions are appreciated. Centrality measures are commonly described as indices of prestige, prominence, importance, and power — the four Ps. However, this sort of interpretation seems inappropriate in the case of sexual networks. In this column, I consider the interpretation of centrality measures in sexual networks, and more generally in the context of any kind of network diffusion. For simplicity, I will assume that the data 1 consist of a discrete symmetric social relation

This note tries to attempt a sketch of the history of spectral ranking—a general umbrella name for techniques that apply the theory of linear maps (in particular, eigenvalues and eigenvectors) to matrices that do not represent geometric transformations, but rather some kind of relationship between entities. Albeit recently made famous by the ample press coverage of Google’s PageRank algorithm, spectral ranking was devised more than fifty years ago, almost exactly in the same terms, and has been studied in psychology and social sciences. I will try to describe it in precise and modern mathematical terms, highlighting along the way the contributions given by previous scholars. Disclaimer This is is a work in progress with no claim of completeness. I have tried to collect evidence of spectral techniques in ranking from a number of sources, providing a unified mathematical framework that should make it possible to understand in a precise way the relationship between contributions. Reports of inaccuracies and missing references are more than welcome. 1

Many classical graph visualization algorithms have already been developed over the past decades. However, these algorithms face difficulties in practice, such as the overlapping node problem, large graph layout and dynamic graph layout. In order to solve these problems, this paper aims to systematically address algorithmic issues related to a novel framework that describes the process of graph visualization applications. First of all, a framework for graph visualization is described. As the important parts of this framework, we then develop two effective algorithms for filtering and clustering large graphs for the layouts. As for the dynamic graph layout, a new approach to removing overlapping nodes called force-transfer algorithm is developed. The framework has been implemented in a prototype called PGA to demonstrate the performance of the proposed algorithms. Finally, a case study is provided.

of biological network centralities with CentiBiN

Methods for ranking World Wide Web resources according to their position in the link structure of the Web are receiving considerable attention, because they provide the first effective means for search engines to cope with the explosive growth and diversification of the Web.