Facebook business

KAIST Software Graduate School

Software Eco system 지도교수 김진형 1

과목 : Software Eco system학번 : 20113920

이름 : 서승현


1. History of Facebook ……………………………………………………………………………

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2. Business Model ……………………………………………………………………………………

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2.1 Ads …………………………………………………………………………………………………

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2.2 Third parties …………………………………………………………………………………

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3. Facebook Technology for Business ………………………………………………………

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3.1 Social Graph …………………………………………………………………………………

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3.2 News Feed(Edge Rank) …………………………………………………………………

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3.3 Social Plugin …………………………………………………………………………………

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4. VS Google …………………………………………………………………………………………

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4.1 Why Google? …………………………………………………………………………………

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4.2 Open Social ……………………………………………………………………………………

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4.3 Page Rank ………………………………………………………………………………………

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5. Future of Facebook ………………………………………………………………………………

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5.1 Network Effect ………………………………………………………………………………

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5.2 Link Predict ……………………………………………………………………………………

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5.3 Open Graph API ……………………………………………………………………………

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5.4 Timeline …………………………………………………………………………………………

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6. Conclusion ……… …………………………………………………………………………………

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6.1 Evolve into Interest Graph ………………………………………………………………

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6.2 Consider with Side Effect ………………………………………………………………

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References



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users

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500 mi

1. History of Facebook


6Software Eco system 지도교수 김진형

2. Business Model 2.1 Ads


Zynga Wants your relationship Friends information

Foursquare Wants your route path Hot Spot

Analyze your pattern

2. Business Model 2.2 Third parties



Facebook’s Connect make Relationship Group Community Echo System Private Web

how they're related

Make Private Web Use Facebook Social Graph Service use Facebook’s Information Share user’s log between Facebook and

Web

3. Facebook Technology for Business

3.1 Social Graph


Newsfeed Latest Posts and Popular Posts Popular Post is default configuration We don’t know where configuration exists Facebook has ‘The Will’ that make private newsfeed

Marketing It is important for Facebook to show ‘Popular Posts’ We saw information to newsfeed than ‘like page’

Support Social Graph Affinity, Weight , Time Decay Analyze relationship


3.2 Edge Rank



3.3 Social Plug-in



Google have Infra and Money already Gmail Searching Engine Android, Motorola

Open Social Platform of Platform It is mine, and yours is mine too

Also launch ‘Google+’ Is Linked in Gmail address And Google docs… and so on

4. VS Google 4.1 Why Google?


State of Union Common API Project Facebook vs Non-Facebook ? App Store vs KT,SK,LG ? Non-Facebook = Google Plus + Open Social = Google

4. VS Google 4.2 Open Social


4. VS Google 4.3 Page Rank


Page Rank equals Google Business Advertisement Searching Trend Prediction Page rank is not unique method but it is important

Collective Intelligence You are not in, but you’re in by Searching Detecting influenza epidemics using search engine query

data SNS consist of active participation Google realize relationship from personal action

4. VS Google 4.3 Page Rank



5. Future of Facebook 5.1 Network Effect


Key Elements People Identity Type of Relationships Relationships Identity

Facebook Power is Network Size The value is dependent on the number of others using. Expect positive feedback more useful the more users join like telephone Ads Target is Relationship and Identity

Consequently, User is Money

5. Future of Facebook 5.1 Network Effect


5. Future of Facebook 5.2 Link Prediction


Method of making more network effect Prevent to leaving platform Lower 30% can’t be adapted Making Group Six degrees of separation Person maybe you know

But, want too much undergo cumbersome feel In fact, don’t like that





Moto is Private web MS-docs, Pandora, Slide-Share Real-time Crawl user’s interest information Private Service Social Plugin is part of Open Graph strategy

Facebook Know your Interest and hobby

Not just Service, be the Platform

But, Alert Privacy

5. Future of Facebook 5.3 Open Graph


5. Future of Facebook 5.3 Open Graph


Term was used in twitter already Allocate contents as time passed Contents Meaningless as time decay

Facebook Say “We give you Memory” Photo, video, etc.. Making diary

5. Future of Facebook 5.4 Timeline



6. Conclusion6.1 Evolve into Interest Graph

6. Conclusion6.1 Evolve into Interest Graph


Interest Prediction

Interest Discovery

Difficult to know what is real interest


6. Conclusion6.2 Consider

with Side Effect


We know Facebook exert ‘Positive Effect’ Wider relationship Interaction Opportunity Information Power of ‘the public’

But, Do you really want ‘cool relationship’? Make friends easily, but lose easily too Many friends, but lonely

Manipulate public opinion

6. Conclusion6.2 Consider

with Side Effect



[1] Facebook’s Edge Rank : How to Make Sure You’re in the News Feed(Buddy Media)

[2] Open Editing Algorithm: A Collaborative News Promotion Algorithm based on Users’ Voting History(2009, GSCT KAIST)

[3] Multi-Relational Link Prediction in Heterogeneous Information Networks(Darcy Davis, Ryan Lichtenwalter, Nitesh V. Chawla ,University of Notre Dame)

[4] Identifying and evaluating community structure in complex networks(2009, Karsten Steinhaeuser, Nitesh V. Chawla , University of Notre Dame)

[5] The Anatomy of a Large-Scale Hypertextual Web Search Engine(Sergey Brin and Lawrence Page ,Stanford University)

[6] The PageRank Citation Ranking: Bringing Order to the Web(Stanford University) [7] The Google Pagerank Algorithm and How It Works(2002, Ian Rogers ,

sirgroane.net) [8]The Link Prediction Problem for Social Networks( 2004, David Liben-Nowell, Jon

Kleinberg ,MIT, Cornell University) [9] IT 정책연구 시리즈 제 16 호 (2011 , @ 한국정보화진흥원 ) [10] The Interest Graph and What Marketers Should Know About It (Stefan Herbert,

Published December 9, 2011) [11] http://en.wikipedia.org/wiki/Positive_feedback

Reference
