1. Analysis of Topological Characteristics of Huge Online Social Networking Services Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, H. Jeong KAIST, Deajeon, South Korea

2. High-Level Questions in the Paper Are online networks similar to offline networks? Are online networks similar to offline networks? What are online networks’ characteristics? What are online networks’ characteristics? Is sampling representative? Is sampling representative? How do online networks evolve? How do online networks evolve? You should all know what they found out… You should all know what they found out…

3. 1. Are these the right questions to ask? 2. Is the evaluation sound? 3. Are the results surprising? My High-Level Questions

4. Question #1: Are these the right questions to ask?

5. Why is it interesting Map new phenomenon Map new phenomenon One interesting study-case One interesting study-case

6. Why is it non-trivial? Or is it…? Or is it…? Data accessibility Data accessibility Sampling analysis Sampling analysis

7. Cyworld as a representative online social network

8. Question #2: Is the evaluation sound?

9. number of triangles connected to vertex i number of triples centered on vertex i 2 |{(v,w)|(i,v)(i,w)(v,w) Є E }| K i ( K i -1) C i = (Newman,SIAM Review 2003) Aside 1: Calculating clustering coeff.

10. Aside 2: Snowball Sampling Under-sample low degree nodes Under-sample low degree nodes Over-sample high degree nodes Over-sample high degree nodes Underestimate power law coefficient Underestimate power law coefficient

11. Underestimating α Estimated K P(K>k) = Fraction of vertices with degree >=k

12. Evaluation Snowball sampling method evaluation Snowball sampling method evaluation More quantitative analysis… More quantitative analysis… -3.2

13. Evaluation : Power law “Clear power-law” : “Clear power-law” :

14. Historical Analysis http://www.internetworldstats.com Internet hosts in Europe http://gandalf.it/data/data2.htm

15. Can the path length be calculated?

16. Question #3: Are the results surprising?

17. Interesting findings Huge social networks are not ‘clean’. Huge social networks are not ‘clean’. Different scaling (= user types?) Different scaling (= user types?) Sampling – some rules of thumb for rations Sampling – some rules of thumb for rations

18. Assortative mixing pattern in social networks Intuitive for race examples in SF, 58’ Intuitive for race examples in SF, 58’ Found to be true even for degree correlation Found to be true even for degree correlation Is it? Is it? Online networks Online networks Other networks Other networks Implications? Implications? Cyworld paper: 6-9/2006 100k users of 33M 0.3% Flicker paper: 10-11/2006 3M users out of 27M 11.3%

19. Questions for discussion Is SK the model representative? Is SK the model representative? Do social networks really display assortative mixing w.r.t degree correlation? Implications Do social networks really display assortative mixing w.r.t degree correlation? Implications How should we analyze networks with multiple user types? Implications? How should we analyze networks with multiple user types? Implications? How do we use findings to leverage How do we use findings to leverage Security (degree of shared interest, reliability) Security (degree of shared interest, reliability) Robustness Robustness Recommendations (beyond friends?) Recommendations (beyond friends?)