We describe Freenet, an adaptive peer-to-peer network application that permits the publication, replication, and retrieval of data while protecting the anonymity of both authors and readers. Freenet operates as a network of identical nodes that collectively pool their storage space to store data files and cooperate to route requests to the most likely physical location of data. No broadcast search or centralized location index is employed. Files are referred to in a location-independent manner, and are dynamically replicated in locations near requestors and deleted from locations where there is no interest. It is infeasible to discover the true origin or destination of a file passing through the network, and difficult for a node operator to determine or be held responsible for the actual physical contents of her own node.

In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequency-based recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agents. 1

Abstract. The web graph is a real-world self-organizing network whose vertices correspond to web pages, and whose edges correspond to links between pages. Many stochastic models for the web graph have been recently proposed, with the aim of reproducing one or more of its observed properties and parameters. Some of the most intensely studied parameters for the web graph are the degree distribution and diameter. A recent stochastic model of the web graph is the protean graph Pn(d, η). In this model, vertices are renewed over time, and older vertices are more likely to receive edges than younger ones. While previous work on the model focussed on the power law degree distribution of protean graphs, in this note we study its diameter. Since the protean graphs may be disconnected, we focus on the diameter of the giant component. Our main result is that diameter of the giant component of Pn(d, η) is equal to Θ(log n), which supports experimental data observed in the actual web graph. 1.

We propose a new method for analyzing multivariate time-series data governed by competitive dynamics such as fluctuations in the number of visitors to Web sites that form a market. To achieve this aim, we construct a probabilistic dynamical model using a replicator equation and derive its learning algorithm. This method is implemented for both categorizing the sites into groups of competitors and predicting the future shares of the sites based on the observed time-series data. We confirmed experimentally, using synthetic data, that the method successfully identifies the true model structure, and exhibits better prediction performance than conventional methods that leave competitive dynamics out of consideration. We also experimentally demonstrated, using real data of visitors to 20 Web sites offering streaming video contents, that the method suggested a reasonable competition structure that conventional methods failed to find and that it outperformed them in terms of predictive performance.