1 / 12

Exploring Small-World Patterns in File-Sharing Communities

This article examines small-world phenomena in diverse file-sharing communities, highlighting their clustering characteristics and average path lengths. It focuses on case studies, including collaborations in high-energy physics and the Kazaa peer-to-peer system. The research explores the data-sharing graph's structure, analyzing user behaviors and connections based on shared requests. By investigating potential biases arising from degree distributions, the study aims to assess inherent patterns in user interaction and their implications for mechanism design in data-sharing environments.

grace
Download Presentation

Exploring Small-World Patterns in File-Sharing Communities

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. presentation of article: Small-World File-Sharing Communities Article: Adriana Iamnitchi, Matei Ripeanu, Ian Foster Presentation: Periklis Akritidis ICS-FORTH

  2. Patterns in file-sharing communities • Small-world patterns exist in diverse file-sharing communities. • A high-energy physics collaboration • The Web as seen from the Boeing traces • The Kazaa peer-to-peer file-sharing system • Motivation: can be exploited for mechanism design.

  3. Data Sharing Graph • A graph in which nodes are users and an edge connects two users with similar interests in data. • Similarity criterion: the number of shared requests within a specified time interval • Degrees of freedom: • length of time interval • threshold on the number of common requests

  4. Small-World Characteristics of Data Sharing Graph • Clustering Coefficient (see article for definition) • Large, much larger than that of a random graph (Poisson distribution for node degree) with the same number of nodes and edges. • Average Path Length • Small, like random graph.

  5. Methodology • Compare clustering coefficients and average path lengths for various communities with random graphs of same size

  6. High Energy Physics Collaboration

  7. Web

  8. Kazaa

  9. Possible Bias: Large clustering coefficient of unimodal affiliation networks • A bipartite network (left) and its unipartite projection (right). • Users A-G access files m-p. • In the unipartite projection, two users are connected if they request the same file. • The projection is inherently more clustered than a random graph.

  10. Possible Bias: degree distribution • Non-poisson degree distribution of data-sharing graphs may cause small-world characteristics • Degree distribution was Zipf for Kazaa and Web • Newman et al. propose a model for random graphs with given degree distributions

  11. Evaluating Bias • Compare against the clustering of unimodal projections of random affiliation networks of the size and degree distributions given by traces. • Results:

  12. User-independent trace characteristics • User-independent characteristics of traces • event frequency follows Zipf distribution • time locality • temporal user activity • Are the observed patterns an inherent consequence of these well-known behaviors? • They processed the traces preserving the documented characteristic but breaking the user-to-request association • The resulting graphs are “less” small-world graphs than their corresponding real ones

More Related