Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Many complex systems display a surprising degree of tolerance against errors. For example, relatively simple organisms grow, persist and reproduce despite drastic pharmaceutical or environmental interventions, an error tolerance attributed to the robustness of the underlying metabolic network [1]. Complex communication networks [2] display a surprising degree of robustness: while key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these and other complex systems is often attributed to the redundant wiring of the functional web defined by the systems ’ components. In this paper we demonstrate that error tolerance is not shared by all redundant systems, but it is displayed only by a class of inhomogeneously wired networks, called scale-free networks. We find that scale-free networks, describing a number of systems, such as the World Wide Web (www) [3–5], Internet [6], social networks [7] or a cell [8], display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected by even unrealistically high failure rates. However,

Abstract. Semantic Web languages are being used to represent, encode and exchange semantic data in many contexts beyond the Web – in databases, multiagent systems, mobile computing, and ad hoc networking environments. The core paradigm, however, remains what we call the Web aspect of the Semantic Web – its use by independent and distributed agents who publish and consume data on the World Wide Web. To better understand this central use case, we have harvested and analyzed a collection of Semantic Web documents from an estimated ten million available on the Web. Using a corpus of more than 1.7 million documents comprising over 300 million RDF triples, we describe a number of global metrics, properties and usage patterns. Most of the metrics, such as the size of Semantic Web documents and the use frequency of Semantic Web terms, were found to follow a power law distribution. 1

this article is to examine, quantify, and characterize the quantity and quality of Web links used in computing literature. Our aim is to provide definitive information related to the availability of URL references as a function of their age, their domain, the depth of the path used, as well as the technical reasons leading to failed links. Our research has been greatly aided by the emergence of online versions of traditional paper-based publications [4]. By tapping into the online libraries of the ACM and the IEEE Computer Society we were able to download, extract, and verify 4,375 Web links appearing in print articles during the period from 1995--1999. Here, we describe the technologies related to Web references and retrieval, outlining the methodology we followed, presenting the results obtained, and discussing their implications

While search engines have become the major decision support tools for the Internet, there is a growing disparity between the image of the World Wide Web stored in search engine repositories and the actual dynamic, distributed nature of Web data. We propose to attack this problem using an adaptive population of intelligent agents mining the Web online at query time. We discuss the benefits and shortcomings of using dynamic search strategies versus the traditional static methods in which search and retrieval are disjoint. This paper presents a public Web intelligence tool called MySpiders, a threaded multiagent system designed for information discovery. The performance of the system is evaluated by comparing its effectiveness in locating recent, relevant documents with that of search engines. We present results suggesting that augmenting search engines with adaptive populations of intelligent search agents can lead to a significant competitive advantage. We also discuss some of the challenges of evaluating such a system on current Web data, introduce three novel metrics for this purpose, and outline some of the lessons learned in the process.

Open access (OA) to the research literature has the potential to accelerate recognition and dissemination of research findings, but its actual effects are controversial. This was a longitudinal bibliometric analysis of a cohort of OA and non-

this paper we demonstrate that such error tolerance is not shared by all redundant systems, but it is displayed only by a class of inhomogeneously wired networks, called scale-free networks. We nd that scale-free networks, describing a number of systems, such as the www [3-5], Internet [6], social networks [7] or a cell [8], display an unexpected degree of robustness, the ability of their nodes to communicate being unaected by even unrealistically high failure rates. However, this error tolerance comes at a high price: these networks are extremely vulnerable to attacks, i.e. to the selection and removal of a few nodes that play the most 1

This paper introduces FASD, a fault-tolerant, adaptive, scalable, and distributed search layer designed to augment existing peer-to-peer applications. The FASD layer operates as a network of identical nodes that collectively pool their storage space to cache "metadata keys" and cooperatively route queries to the nodes most likely to satisfy them. A "metadata key" is a list of weighted terms that describe the information content of a document in the underlying network. Although completely decentralized, FASD's approach is able to e#ciently match the recall and precision of a centralized search engine. Simulation results indicate that latency and bandwidth consumption scale logarithmically with the size of a FASD network.

Reliable communication on the Internet is guaranteed by a standard set of protocols, used by all computers. We show that these protocols can be exploited to compute with the communication infrastructure, transforming the Internet into a distributed computer in which servers unwittingly perform computation on behalf of a remote node. In this model, which we call parasitic computing, one machine forces target computers to solve a piece of a complex computational problem merely by engaging them in standard communication. Consequently, the target computers are unaware that they have performed computation for the benefit of a commanding node. To offer experimental evidence of the principle of parasitic computing, we harness the power of several web servers across the globe, that, unknown to them, work together to solve an NP complete problem. Parasitic computing raises important questions about the ownership of the resources connected to the Internet and challenges current computing paradigms. For more than a millennium, scientists have been devising computing machines that represent and manipulate symbolic information for solving complex problems. Minimally, symbolic information can be represented within a machine as a physically-discernible state in nature, be it the

Abstract. This paper argues that the growing importance of the World Wide Web means that Web sites are key candidates for digital preservation. After an brief outline of some of the main reasons why the preservation of Web sites can be problematic, a review of selected Web archiving initiatives shows that most current initiatives are based on combinations of three main approaches: automatic harvesting, selection and deposit. The paper ends with a discussion of issues relating to collection and access policies, software, costs and preservation. 1