Skip to main content

Nonbacktracking bounds on the influence in independent cascade models

Author(s): Abbe, Emmanuel; Kulkarni, Sanjeev; Lee, EJ

To refer to this page use:
Abstract: 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 type inequalities, and are computed by message passing algorithms. 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 parameterized versions of the bounds that control the trade-off between the efficiency and the accuracy. Finally, the tightness of the bounds is illustrated with simulations on various network models.
Publication Date: 2017
Citation: Abbe, E, Kulkarni, S, Lee, EJ. (2017). Nonbacktracking bounds on the influence in independent cascade models. 2017-December (1408 - 1417
Pages: 1408 - 1417
Type of Material: Conference Article
Journal/Proceeding Title: Advances in Neural Information Processing Systems
Version: Author's manuscript

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.