Information Diffusion and External Influence in Networks

This Paper is available online [1].

Summary

This paper is unique in that it investigates external influence as a factor of information spread in social networks, rather than just diffusion as most previous work had done. The authors propose a model which allows information to spread from one node to another in the graph according to a probabilistic model. Additionally, it allows for information to spread from outside of the graph network also from a probability distribution. Furthermore, even though a node has neighbors that may allow information to diffuse, it may still have a generative story that the information came from an external source. They evaluate their results on a dataset of Twitter - using ever tweet from one month. Internal diffusion within the network can occur when one person tweets the same link as someone that he/she is following. External influence is when something enters the social graph, even if it is the same piece of information elsewhere in the graph if the tweeter does not directly follow them, or possibly if they happened to find the same information as someone they follow, but not through their tweets.