Small-World File-Sharing Communities

TL;DR

This paper introduces the data-sharing graph, revealing small-world patterns in user data interests across various systems, which can be exploited to improve system performance.

Contribution

It proposes a novel data-sharing graph structure and demonstrates its small-world properties in multiple real-world communities, offering new insights for system design.

Findings

Small-world patterns are present in data-sharing graphs of different communities.

The structure of these graphs can inform better data distribution mechanisms.

Emergent patterns suggest potential for optimizing peer-to-peer and distributed systems.

Abstract

Web caches, content distribution networks, peer-to-peer file sharing networks, distributed file systems, and data grids all have in common that they involve a community of users who generate requests for shared data. In each case, overall system performance can be improved significantly if we can first identify and then exploit interesting structure within a community's access patterns. To this end, we propose a novel perspective on file sharing based on the study of the relationships that form among users based on the files in which they are interested. We propose a new structure that captures common user interests in data--the data-sharing graph-- and justify its utility with studies on three data-distribution systems: a high-energy physics collaboration, the Web, and the Kazaa peer-to-peer network. We find small-world patterns in the data-sharing graphs of all three communities. We analyze these graphs and propose some probable causes for these emergent small-world patterns. The significance of small-world patterns is twofold: it provides a rigorous support to intuition and, perhaps most importantly, it suggests ways to design mechanisms that exploit these naturally emerging patterns.