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M acroscopic Exploration of the Twitter Social Graph. Maksym Gabielkov, Arnaud Legout EPI DIANA, Sophia Antipolis { maksym.gabielkov , a rnaud.legout }@inria.fr. How information propagate?. What are the influence mechanisms?. Friends. Producer Consumers. Follow R elationship in Twitter.
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Macroscopic Exploration of the Twitter Social Graph Maksym Gabielkov, Arnaud Legout EPI DIANA, Sophia Antipolis {maksym.gabielkov, arnaud.legout}@inria.fr
How information propagate? What are the influence mechanisms?
Producer Consumers
FollowRelationship in Twitter Bob follows Alice Alice follows Bob Bob Alice
The Twitter Social Graph Bob Alice
+500 millions nodes +24 milliards edges Challenges • Collect the graph • Decompose the graph • Give a physical meaning to the decomposition
How is constraint information propagation? Identify the highways
1 1 1 1 1 1 4 1 3 3 4 1 1 1
1 1 1 1 1 1 4 1 3 3 4 1 1 1
250 millions 250 millions 726 000
Macrostructure of the Twitter social graph Directed acyclic graph SCC decomposition
OUT-TENDRILS OTHER IN-TENDRILS BRIDGES LSC OUT IN DISCONNECTED
Macrostructure of the Twitter social graph Directed acyclic graph Macrostructure
1% accounts <0.01% edges <0.01% tweets
98% of the tweets 98% of the edges 50% of the accounts Regular activity
1,5% of the tweets 5,3% of the accounts 0% outgoingedges Selfishcelebrities
21,4% of the accounts 0,25% of the tweets Passive users
21,6% of the accounts 99% no edge 80% no tweet Spammeurs
Macroscopic Exploration of the Twitter Social Graph Maksym Gabielkov, Arnaud Legout EPI DIANA, Sophia Antipolis {maksym.gabielkov, arnaud.legout}@inria.fr
Following et follower following following follower follower Bob Alice Bob Alice
Twitter in 2009 • 41.7 million users • 1.47 billion follow links • Average degree: 35 • Partialcrawls • Twitter in 2012 • 537 million users • 23.95 billion follow links • Average degree: 44 • Completecrawl