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Analysis of Topological Characteristics of Huge Online Social Networking Services

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. High-Level Questions in the Paper. Are online networks similar to offline networks? What are online networks’ characteristics?

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Analysis of Topological Characteristics of Huge Online Social Networking Services

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  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? • What are online networks’ characteristics? • Is sampling representative? • How do online networks evolve? • You should all know what they found out…

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

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

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

  6. Why is it non-trivial? • Or is it…? • Data accessibility • 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) ЄE }| Ki (Ki -1) Aside 1: Calculating clustering coeff. Ci = (Newman ,SIAM Review 2003) Ci =

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

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

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

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

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

  15. Can the path length be calculated?

  16. Question #3:Are the results surprising?

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

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

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

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