presentation of article: Small-World File-Sharing Communities

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