Information propagation in social networks
This study delves into the dynamics of information spreading within social networks, highlighting the interconnectedness among users through activities like following relationships. The exploration involves a large-scale analysis focusing on data from platforms like Twitter, aiming to unravel patterns and challenges in information dissemination. From dissecting the network structure to identifying key components, the research sheds light on the complexities of online information flow and its implications.
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Information propagation in social networks Maksym Gabielkov, Ashwin Rao, Arnaud Legout EPI DIANA, Sophia Antipolis {maksym.gabielkov, arnaud.legout}@inria.fr
Producer Consumers
Follow Relationship in Twitter Bob follows Alice Alice follows Bob Alice Bob
The Twitter Social Graph Alice Bob
+500 million nodes +24 billion edges Challenges 1. Collect the graph 2. Decompose the graph 3. 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
Directed acyclic graph 249 million nodes Twitter social graph 500 million nodes
OUT-TENDRILS OTHER IN-TENDRILS BRIDGES LSC OUT IN DISCONNECTED
Directed acyclic graph 249 million nodes Twitter social graph 500 million nodes Macro structure 8 components
What is the physical meaning of decomposition?
1% accounts <0.01% edges <0.01% tweets
98% of the tweets 98% of the edges 50% of the accounts
1,5% of the tweets 5,3% of the accounts 0% outgoing edges
21,4% of the accounts 0,25% of the tweets
21,6% of the accounts 99% no edge 80% no tweet
Information propagation in social networks Maksym Gabielkov, Ashwin Rao, Arnaud Legout EPI DIANA, Sophia Antipolis {maksym.gabielkov, arnaud.legout}@inria.fr
Twitter in 2009 41.7 million users 1.47 billion follow links Average degree: 35 Partial crawls Twitter in 2012 537 million users 23.95 billion follow links Average degree: 44 Complete crawl
