On the approximability of influence in social networks
By Ning Chen. ACM-SIAM Symposium on Discrete Algorithms, 2008
415 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/d48a86b59a29bab943e6fbf63877fa5c9dcf26f1
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In Computational Theory and Mathematics
- Jennifer Tour Chayes
- Christian Borgs
- Yadati Narahari
- Brendan Lucier
- Dexter Campbell Kozen
- Alex Collins
- David Rincón
- Zhenming Liu
In Statistical and Nonlinear Physics
- Yadati Narahari
- Ramasuri Narayanam
- Yajun Wang
- Alex Collins
- David Rincón
- Zhenming Liu
- Wei Wei
- Xiaorui Sun
Citations in the corpus
- Design and Analysis of Algorithms (2009, methodology): “1 of [5] (decomposition into edge disjoint small subgraphs) we deduce that for sufficiently large t,”
- A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks (2011, methodology): “Chen et al. [10] present an efficient algorithm to find the topnodes in a social network and this algorithm improves upon the greedy algorithm of Kempe et al. [28] and also the algorithm of Leskovec et al. [34] in terms of its running time.”
- Maximizing Social Influence in Nearly Optimal Time (2012, background): “In contrast, many other formulations of the influence maximization problem have been shown to have strong lower bounds on polynomial-time approximability [33, 31, 11, 4].”
- Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate (2011, methodology): “The work was done when all authors were working at or visiting Microsoft Research Asia.”
- Efficient influence maximization in social networks (2009, methodology): “Following the approach of [6] as well as later studies [7, 8, 9, 10, 1], we formulate the influence maximization problem as an optimization problem given a network structure and an influence cascade model, which are two key factors affecting the outcome of an influence propagation.”
- Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks (2016, background): “Each edge (u, v) ∈ E is associated with a weight w(u, v) ∈ [0, 1] which indicates the probability that u influences v.”