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

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Page 1: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Analysis of Topological Analysis of Topological Characteristics of Huge Characteristics of Huge

Online Social Networking Online Social Networking ServicesServices

Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, H. JeongY.-Y. Ahn, S. Han, H. Kwak, S. Moon, H. JeongKAIST, Deajeon, South KoreaKAIST, Deajeon, South Korea

Page 2: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

High-Level Questions in the High-Level Questions in the PaperPaper

Are online networks similar to offline Are online networks similar to offline networks?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 You should all know what they found out…out…

Page 3: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

1.1. Are these the right questions to ask?Are these the right questions to ask?

2.2. Is the evaluation sound?Is the evaluation sound?

3.3. Are the results surprising?Are the results surprising?

My High-Level QuestionsMy High-Level Questions

Page 4: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Question #1:Question #1:Are these the right questions to Are these the right questions to

ask?ask?

Page 5: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Why is it interestingWhy is it interesting

Map new phenomenonMap new phenomenon

One interesting study-case One interesting study-case

Page 6: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Why is it non-trivial?Why is it non-trivial?

Or is it…? Or is it…?

Data accessibilityData accessibility

Sampling analysisSampling analysis

Page 7: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Cyworld as a representative Cyworld as a representative online social network online social network

Page 8: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Question #2:Question #2:Is the evaluation sound?Is the evaluation sound?

Page 9: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

number of triangles connected to vertex inumber of triples centered on vertex i

2 |{(v,w)|(i,v)(i,w)(v,w) Є E }|Ki (Ki -1)

Ci =

Ci =

(Newman ,SIAM Review 2003)

Aside 1: Calculating clustering Aside 1: Calculating clustering coeff. coeff.

Page 10: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Aside 2: Snowball SamplingAside 2: Snowball Sampling Under-sample low degree nodesUnder-sample low degree nodes

Over-sample high degree nodesOver-sample high degree nodes

Underestimate power law Underestimate power law coefficientcoefficient

Page 11: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Underestimating Underestimating αα

Estimated

K

P(K>k) = Fraction of

vertices with degree >=k

Page 12: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

EvaluationEvaluation Snowball sampling method evaluationSnowball sampling method evaluation

More quantitative analysis…More quantitative analysis…

-3.2

Page 13: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Evaluation : Power lawEvaluation : Power law ““Clear power-law” :Clear power-law” :

Page 14: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Historical AnalysisHistorical Analysis

http://www.internetworldstats.com

Internet hosts in Europehttp://gandalf.it/data/data2.htm

Page 15: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Can the path length be Can the path length be calculated? calculated?

Page 16: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Question #3:Question #3:Are the results surprising?Are the results surprising?

Page 17: Analysis of Topological Characteristics of Huge Online Social Networking Services€¦ · PPT file · Web view · 2007-09-27Analysis of Topological Characteristics of Huge Online

Interesting findingsInteresting 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 Sampling – some rules of thumb for rationsrations

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Assortative mixing pattern in Assortative mixing pattern in social networkssocial networks

Intuitive for race examples in SF, Intuitive for race examples in SF, 58’58’

Found to be true even for degree Found to be true even for degree correlation correlation

Is it? Is it? Online networksOnline networks Other networksOther networks

Implications?Implications?Cyworld paper:6-9/2006100k users of 33M0.3%

Flicker paper:10-11/2006 3M users out of 27M 11.3%

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Questions for discussionQuestions for discussion Is SK the model representative? Is SK the model representative?

Do social networks really display assortative Do social networks really display assortative mixing w.r.t degree correlation? Implications mixing w.r.t degree correlation? Implications

How should we analyze networks with multiple How should we analyze networks with multiple user types? Implications? 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?)