Measurement and Analysis of the Direct Connect Peer-to-Peer File Sharing Network
Online social networks and peer-to-peer file sharing networks create a digital mirror of human society, providing insights in social dynamics such as interaction between entities, structural patterns and flow of information. In the past such studies were inherently limited due to the vast supply of information. Today these phenomena can be studied at large scale using computers to process data from this digital mirror. Findings from such networks have shown interesting structural properties shared by both types of systems. In particular, it is often the case that they show to be scale-free and small-world networks. By letting ideas and findings from studied peer-to-peer networks guide the design of novel architectures, improvements on user integrity, usability and performance have been observed. This thesis presents a study of the Direct Connect peer-to-peer file sharing network. We model abstract tools and methods for measuring the network architecture, and, moreover, custom software tools for data gathering and analysis from Direct Connect networks are developed, presented and discussed. We look at network topology and properties, statistics on user activities and geographic distribution, characterization/statistics on data shared and correlations of users and their shared data. We verify the scale-free property, small-world network model, strong data redundancy with clusters of common interest in the set of shared content, high degree of asymmetry of connections and more. Finally, we discuss the implications of our findings and comparison with results from similar research is done.