Nonbacktracking Bounds on the Influence in Independent Cascade Models
This provides improved computational methods for influence estimation in social networks, though it appears incremental as it extends existing nonbacktracking techniques to a new application.
The paper develops upper and lower bounds on the influence measure in networks using the independent cascade model, exploiting nonbacktracking walks and FKG-type inequalities with a message passing implementation, and demonstrates their tightness through simulations on various network models.
This paper develops upper and lower bounds on the influence measure in a network, more precisely, the expected number of nodes that a seed set can influence in the independent cascade model. In particular, our bounds exploit nonbacktracking walks, Fortuin-Kasteleyn-Ginibre (FKG) type inequalities, and are computed by message passing implementation. Nonbacktracking walks have recently allowed for headways in community detection, and this paper shows that their use can also impact the influence computation. Further, we provide a knob to control the trade-off between the efficiency and the accuracy of the bounds. Finally, the tightness of the bounds is illustrated with simulations on various network models.