Learning the Nature of Information in Social Networks


  • Rakesh Agrawal Microsoft
  • Michalis Potamias Groupon
  • Evimaria Terzi Boston University




information propagation, social networks


We postulate that the nature of information items plays a vital role in the observed spread of these items in a social network. We capture this intuition by proposing a model that assigns to every information item two parameters: endogeneity and exogeneity. The endogeneity of the item quantifies its tendency to spread primarily through the connections between nodes; the exogeneity quantifies its tendency to be acquired by the nodes, independently of the underlying network. We also extend this item-based model to take into account the openness of each node to new information. We quantify openness by introducing the receptivity of a node. Given a social network and data related to the ordering of adoption of information items by nodes, we develop a maximum-likelihood framework for estimating endogeneity, exogeneity and receptivity parameters. We apply our methodology to synthetic and real data and demonstrate its efficacy as a data-analytic tool.




How to Cite

Agrawal, R., Potamias, M., & Terzi, E. (2021). Learning the Nature of Information in Social Networks. Proceedings of the International AAAI Conference on Web and Social Media, 6(1), 2-9. https://doi.org/10.1609/icwsm.v6i1.14257