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

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The Model II• There is a “global list” in which “global tweets”arrive with frequency λ 1 (distributed as aPoisson point process). Each of these bringsa new topic.• Each user has a “local list” into which tweetsare written with frequency λ 2 (distributed as aPoisson point process).The topic of a user’s tweet is chosen randomlyout of the topics in the global list and the locallists of its neighbours in the network.
The Model III• Each global tweet has a weight A on arrival inthe global list.• This weight decreases exponentially withtime with parameter α i.e. Ae -αt at time t if thetopic arrived at time 0.• When a user tweets then that tweet is placedin its local list with weight B.• This weight decreases exponentially withtime with parameter β i.e. Be -βt at time t if thetweet arrived at time 0.
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: The Model I• Users form a network
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 and 28: Large clusters in viral topics10 41
Page 29 and 30: The real data also has geography10
Page 31 and 32: The emerging science of big data•
Page 33 and 34: The research horizon..• …is unl

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

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