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Nonbacktracking Bounds on the Influence in Independent Cascade Models
Emmanuel Abbe · Sanjeev Kulkarni · Eun Jee Lee

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #182

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 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.

Author Information

Emmanuel Abbe (Princeton University)
Sanjeev Kulkarni (Princeton University)
Eun Jee Lee (Princeton University)

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