Information Diffusion in Social Networks - School of Technology and ...

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Cluster merging in the modelMax/2ndMax Cluster Size32.521.550180 85 90 95 100 105 110 115 120 125 0TimeMax/2ndMaxEvolution500450400350300250200150100EvolutionMax/2ndMax Cluster Size7000600050004000300020001000800070006000500040003000200010000400 450 500 550 600 650 700 750 800 850 900 950 0TimeMax/2ndMaxEvolutionEvolutionThe ratio of the largest to the second largestcomponent in the evolving graph tells a story.
The real data also has geography10 310 210 110 4 0 10 20 30 40 50 60 70 80 0.740.810.07Topic users countTemporalGeographical0.80.790.780.770.760.75Fraction of edges across countriesTopic users count10 110 010 2 0 10 20 30 40 50 60 70 80TemporalGeographical0.060.050.040.030.020.010Fraction of edges across countriesDayDayViral topics cross regional/national boundariesin the cumulative evolving graph.
Page 1 and 2: Information Diffusion inSocial Netw
Page 3 and 4: Research aspects of OSNs• Sociolo
Page 5 and 6: Two aspects: Macro and MicroMicro:
Page 7 and 8: SIR: The Macro questionClearly, as
Page 9 and 10: The way of EngineeringUse the obser
Page 11 and 12: Refining the questionThe Attributio
Page 13 and 14: The Model I• Users form a network
Page 15 and 16: The Model III• Each global tweet
Page 17 and 18: A reality check• The total weight
Page 19 and 20: Evolution in the viral regime800070
Page 21 and 22: Live longer, go further800070006000
Page 23 and 24: Topological study: Viral topicsMax
Page 25 and 26: Empirical cross-verification: Setup
Page 27: Large clusters in viral topics10 41
Page 31 and 32: The emerging science of big data•
Page 33 and 34: The research horizon..• …is unl

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