The world-wide web forms a large directed graph, whose vertices are documents and edges are links pointing from one document to another. Here we demonstrate that despite its apparent random character, the topology of this graph has a number of universal scale-free characteristics. We introduce a model that leads to a scale-free network, capturing in a minimal fashion the self-organization processes governing the world-wide web.

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The science of social networks is a central field of sociological study, a major application of random graph theory, and an emerging area of study by economists, statistical physicists and computer scientists. While these literatures are (slowly) becoming aware of each other, and on occasion drawing from one another, they are still largely distinct in their methods, interests, and goals. Here, my aim is to provide some perspective on the research from these literatures, with a focus on the formal modeling of social networks and the two major types of models: those based on random graphs and those based on game theoretic reasoning. I highlight some of the strengths, weaknesses, and potential synergies between these two network modeling approaches.

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.

Introduction A combination of Internet use and home computing have increasingly moved activities once almost exclusively ascribed to the public realm into the private home. It is increasingly possible to work, shop and participate in leisure activities all from within the refuge of the private residence. Computer-mediated communication allows for greater connectivity to resources and information, but simultaneously it may be disconnecting us from members of our social networks and reducing public participation. As globally connected as the Internet is the technology necessary for participation is inherently local, primarily available at work, school and increasing from home. Will the location of new information and communication technology in the home isolate us from our local surroundings? How will computer-mediated communication effect social relations at the local level? Netville The ideal setting to research the effects of home-centered communication and i

Connections in distributed systems, such as social networks, online communities or peer-to-peer networks, form complex graphs. These graphs are of interest to scientists in fields as varied as marketing, epidemiology and psychology. However, knowledge of the graph is typically distributed among a large number of subjects, each of whom knows only a small piece of the graph. Efforts to assemble these pieces often fail because of privacy concerns: subjects refuse to share their local knowledge of the graph. To assuage these privacy concerns, we propose reconstructing the whole graph privately, i.e., in a way that hides the correspondence between the nodes and edges in the graph and the real-life entities and relationships that they represent. We first model the privacy threats posed by the private reconstruction of a distributed graph. Our model takes into account the possibility that malicious nodes may report incorrect information about the graph in order to facilitate later attempts to de-anonymize the reconstructed graph. We then propose protocols to privately assemble the pieces of a graph in ways that mitigate these threats. These protocols severely restrict the ability of adversaries to compromise the privacy of honest subjects.

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We analyze growing networks ranging from collaboration graphs of scientists to the network of similarities defined among the various transcriptional profiles of living cells. For the explicit demonstration ofthe scale-free nature and hierarchical organization of these graphs, a deterministic construction is also used. We demonstrate the use of determining the eigenvalue spectra of sparse random graph models for the categorization of small measured networks.