Visualization of evolutionary cascades of messages using force-directed graphs
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Visualization of evolutionary cascades of messages using
force-directed graphs
Master’s thesis defense, 9 may 2011
Artjom KurapovSupervisor: Helena Kruus
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Agenda
Background Practical work
Pling.ee,opensource Gephi Web-tool demo and twitter
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Background
Types of networks Properties / areas of application Research interest
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Topics crossroads
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Goals
Visualize social networks (preferably in Estonia) Compare friends and messages topology Try to mine data visually using cascades
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Pling
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Pling – Qualitative measure
Friends MessagesAverage clustering coefficient 0.135 0.043
Average degree 4.313 2.202
GCC diameter 20 38
Average GCC diameter 5.38 13.009
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Topic and interface matters
Out of 18.6 mln messages - no (clearly visible) cascade
Possibly because 89% private 86% sent using phone
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Javascript tool
Up to 1000 nodes Can add nodes on the fly Navigation and filtering Properties calculation Recursive algorithm
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Friendship and message network mined 218 users / 12643 messages, 6.89% retweets
0 2 3 4 5 7 81
10100
100010000
100000
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Thank you
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Questions?