Seeding with Costly Network Information

@article{Eckles2022SeedingWC,
  title={Seeding with Costly Network Information},
  author={Dean Eckles and Hossein Esfandiari and Elchanan Mossel and M. Amin Rahimian},
  journal={Operations Research},
  year={2022}
}
In the presence of contagion, decision makers strategize about where in a network to intervene (e.g., seeding a new product). A large literature has developed methods for approximately optimizing the choice of k seeds to cause the largest cascade of, for example, product adoption. However, it is often impractical to measure an entire social network. In “Seeding with Costly Network Information,” Eckles, Esfandiari, Mossel, and Rahimian develop and analyze algorithms for making a bounded number… 

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References

SHOWING 1-10 OF 90 REFERENCES
Heuristic Algorithms for Influence Maximization in Partially Observable Social Networks
TLDR
A set of novel heuristic algorithms that specifically target nodes at this boundary, in order to maximize influence across the whole network are proposed and show that these algorithms outperform the state of the art by up to 38% in networks with partial observability.
Scalable Methods for Adaptively Seeding a Social Network
TLDR
Algorithms for linear influence models with provable approximation guarantees that can be gracefully parallelized are developed, and adaptive seeding is shown to be scalable and to obtains dramatic improvements over standard approaches of information dissemination.
Maximizing the Spread of Influence through a Social Network
TLDR
The present article is an expanded version of two conference papers, which appeared in KDD 2003 and ICALP 2005, which provide the first provable approximation guarantees for efficient algorithms.
Sketch-based Influence Maximization and Computation: Scaling up with Guarantees
TLDR
This work develops a novel sketch-based design for influence computation, called SKIM, which scales to graphs with billions of edges, with one to two orders of magnitude speedup over the best greedy methods.
Influence Maximization with an Unknown Network by Exploiting Community Structure
TLDR
This work presents the ARISEN algorithm, which leverages community structure to find an influential seed set by querying only a fraction of the network, and demonstrates its performance on real world networks of homeless youth, village populations in India, and others.
Evaluating stochastic seeding strategies in networks
TLDR
This paper considers contrasts between stochastic seeding strategies and analyze nonparametric estimators adapted from policy evaluation and importance sampling, and uses simulations on real networks to show that the proposed estimators and designs can substantially increase precision while yielding valid inference.
Maximizing Influence in an Unknown Social Network
TLDR
This work introduces exploratory influence maximization, in which an algorithm queries individual network nodes (agents) to learn their links to locate a seed set nearly as influential as the global optimum using very few queries.
Scalable influence maximization for independent cascade model in large-scale social networks
TLDR
This article designs a new heuristic algorithm that is easily scalable to millions of nodes and edges and significantly outperforms all other scalable heuristics to as much as 100–260% increase in influence spread.
Maximizing Social Influence in Nearly Optimal Time
TLDR
This work addresses the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, under the standard independent cascade model of network diffusion.
Influence Maximization in Near-Linear Time: A Martingale Approach
TLDR
The proposed influence maximization algorithm is a set of estimation techniques based on martingales, a classic statistical tool that provides the same worst-case guarantees as the state of the art, but offers significantly improved empirical efficiency.
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