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

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Studying topology empiricallyWe define topic based graphs for each topic• Lifetime graph: The subgraph induced by allusers who have ever tweeted on the topic.• Evolving graphs: The sequence of graphsinduced by the users who tweet on the topicon a given day.• Cumulative evolving graph: There is anedge from u to v if u follows v and u tweetson the topic a day after tweets on day t and
Topological study: Viral topicsMax Cluster Sizes/ Evolution80007000600050004000300020001000Max Cluster Size2ndmax Cluster Size3rdmax Cluster SizeEvolutionNo. of Clusters500400 450 500 550 600 650 700 750 800 850 900 950 0TimeFor a viral topic clusters merge into one as itrises in popularity. (Evolving graph)350300250200150100Number of Clusters
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: Live longer, go further800070006000
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

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