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

2. 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. 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. 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. 5 Methodology Compare clustering coefficients and average path lengths for various communities with random graphs of same size

6. 6 High Energy Physics Collaboration

7. 7 Web

8. 8 Kazaa

9. 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. 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. 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. 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