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Seeding influential nodes in non-submodular models of information diffusion

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Abstract

We consider the model of information diffusion in social networks from Hui et al. (Agentbased simulation of the diffusion of warnings, 2010) which incorporates trust (weighted links) between actors, and allows actors to actively participate in the spreading process, specifically through the ability to query friends for additional information. This model captures how social agents transmit and act upon information more realistically as compared to the simpler threshold and cascade models. However, it is more difficult to analyze, in particular with respect to seeding strategies. We present efficient, scalable algorithms for determining good seed sets—initial nodes to inject with the information. Our general approach is to reduce our model to a class of simpler models for which provably good sets can be constructed. By tuning this class of simpler models, we obtain a good seed set for the original more complex model. We call this the projected greedy approach because a model is ‘projected’ onto a class of simpler models where the greedy seed set selection is near-optimal. We demonstrate the effectiveness of our seeding strategy on synthetic graphs as well as a realistic San Diego evacuation network constructed during the 2007 fires, and the DBLP network of collaborations.

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Notes

  1. Choosing a seed set to maximize a diffusion belongs to the class of NP-hard problems, a class for which there are no known efficient procedures. A procedure is efficient if it runs in time that is polynomial in the size of the social network. For practical purposes an algorithm that takes longer than the cube of the network size is already not feasible on population scale networks. Since the only known algorithms that maximize a diffusion are exponential, such algorithms are far from feasible.

  2. A node here is assumed to fuse all the information received from the same source into a single value, and thus cannot get “overwhelmed” or “congested” with too much information.

  3. For example if, when a node \(u_i\) propagates an information-value set to \({u}_{j}\), it is received at the other end with some probability \(p(i,j)\) depending on the communication infrastructure, then the number of nodes which are ultimate believers is a random variable.

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Acknowledgments

Anshelevich was partially supported by NSF grants CCF-0914782, CNS-1017932, and CCF-1101495 and Magdon-Ismail was partially supported by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053.

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  1. Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY, USA

    Elliot Anshelevich, Ameya Hate & Malik Magdon-Ismail

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  1. Elliot Anshelevich
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Correspondence to Elliot Anshelevich.

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Portions of this work appeared as a short paper in AAMAS 2013.

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Anshelevich, E., Hate, A. & Magdon-Ismail, M. Seeding influential nodes in non-submodular models of information diffusion. Auton Agent Multi-Agent Syst 29, 131–159 (2015). https://doi.org/10.1007/s10458-014-9254-4

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