A Novel Algorithm for Information Cascade Prediction using Point Processes

The application of point processes for information cascade prediction in social networks involves modeling the temporal dynamics of information propagation. Point processes, such as Hawkes processes, capture the self-exciting nature of events and inter-event dependencies. By analyzing past cascades, these models predict future information diffusion patterns, aiding in understanding and managing viral content spread in dynamic social network environments. This approach contributes valuable insights to fields like marketing, epidemiology, and online influence dynamics.