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Einstein’s Legacy

Einstein’s Legacy. Introduction. Inktomi Never chose create its own portal June 2000 Stockvalue dropped by $2.8 bil Yahoo! Has fired Replaced Inktomi with google Almost a new comer. Google. Larry Page-Cofounder ’’ I’m feeling lucky ’’ Addiction No wonder why yahoo chose google. 1.

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Einstein’s Legacy

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  1. Einstein’s Legacy

  2. Introduction • Inktomi • Never chose create its own portal • June 2000 Stockvalue dropped by $2.8 bil • Yahoo! Has fired • Replaced Inktomi with google • Almost a new comer

  3. Google • Larry Page-Cofounder • ’’ I’m feeling lucky ’’ • Addiction • No wonder why yahoo chose google.

  4. 1 • Google intrigued barabasi becoz it voilated the basic prediction of Scale free model. • First mover has the advantage. • In SFM the most connected nodes are that appear first. • Google launched in 1997 overtook the market with in 3 years. • Competetors were AltaVista and Inktomi.

  5. History of bussiness • Apple • Aircraft Industry • ’De Havilland’ – jet powered plane –Comet • In 1949-450 miles/hr(Euro and American Mkt) • Disasters after one year • Chance for Boeing to take the market • Boeing 707(5 years after Comet)-40 yrs • The 3rd ’ The European Airbus’ entered. • Shrinking the market of Boeing

  6. SFM has no such place for dominant late comers. • They assumed that ’all nodes are identical’ • In complex networks each node has a unique characteristics. • Some show up late and grab the market • Some rise early yet never make it • So if we want to calculate this competition in networks , we have to know that each node is different.

  7. 2 • Knack,Loyal partners & addicts(common?) • Hard to find the universal secret, but we can point out certain char that separate the winners from losers. • In competetion each node should has a Fitness • Fitness is a quantitative measure of a nodes ability to stay in front of competetion

  8. 2 basic mechanisms governing the n/w evolution. • Growth • Preferential Attachment • Fitness doesn’t eliminate these two • But it changes in a competitive environment • Incorporate fitness into the SFM is to assume preferencial attachment is driven by the product of nodes fitness and number of links it has

  9. Each node decides where to link by comparing the fitness connectivity prod. • The probability to connect to a new node • In SFM – k/EiKi • The Fitness model has an additional char ’n’ – kn/EiKin. • If Two nodes with same no of links the fittest one aqires • If 2 nodes has the same fitness then the older one has an advantage

  10. 3 • Ginestra Bianconi • Ist year graduate in Ph.d. • Author asked her to study the properties of fitness model. • She was born & educated in rome & she is very good at statistical mechanics • She confirmed that in the presence of fitness the early bird is not necessaryly the winner.

  11. The fitness model tells nodes still aquire links following a power law • So the speed at which nodes aquire links is nomatter of seniority • Beauty over Age • Dymanic Picture • SFM – Narrow lane highway • Fitness Model – Multilane highway

  12. The fitness model posed new questions • Would the power law apply for the fitness model? • Would networks driven by competition continue to be scale free? BREAK

  13. The Three Giants • To understand how competion shapes the networks topology our author took us back in to the time of three giants of Quantum theory • Albert Einstein • Max Plank • Satyendranath bose

  14. Eric A.Cornell And Carl E. weiman In 1995 Attained the temps of Bose-einstein condensation needs. Solved the mystery by condensing the rubidium atoms. Nobel Prizes in after six years ie..in 2001. From then B-E condensation became the physicists toolkit. Bianconi reached this toolkit.

  15. The Mathematical transformation Biaconi sustituted for fitness of energy E = (-1/B) log n • So here in this mapping complex networks are like huge quantum gas , their links behave like subatomic paricles • The prediction was – some n/w undergo Bose-Einstein Condensation • The consequense is in some networks the winner can take all

  16. Two Categories • Fit – Get – Rich • Despite the feirce competetion for links , the scale free topology survives • Winner takes all • The fittest node grabs all links • Not a scale free network • In reality we cannot find the second category • Even the Google example falls under the Fit-get-rich category

  17. Is there any real time networks that come under the Winner takes all category? • The answer is ’Windows’ • Gates and Allen

  18. Wraping Up / Conclusion • As long as we thought of networks as random we modeled them as STATIC graphs • The SFM reflects our awakening to the reality that networks are Dynamic systems that change constantly through the addition of new nodes and links. • The fitness model allows us to describe networks as competive systems in which nodes fight fiercely for links. • Now Bose-Einstein Condensation explains how some winners get the chance to take it all.

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