A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks

By Ramasuri Narayanam, Yadati Narahari. IEEE Transactions on Automation Science and Engineering, 2011

394 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Identifying a set of influential spreaders in complex networks (2016, methodology): “Based on greedy strategy, Narayanam and Narahari [24] proposed a much faster algorithm SPIN approach than greedy algorithm while its quality decreasing a little.”
Influence Maximization on Social Graphs: A Survey (2018, background): “Narayanam and Narahari propose the ShaPley value-based Inﬂuential Nodes algorithm (SPIN) for the LT model in [60].”
Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate (2011, background): “…with a standard greedy approximation algorithm while being orders of magnitude faster. keywords: influence maximization; social networks; negative opinions; independent cascade model; ∗Author affiliations and emails: W. Chen (contact author), Microsoft Research Asia, China, weic@microsoft.com.”
IRIE: Scalable and Robust Influence Maximization in Social Networks (2011, methodology): “To estimate influence function σ(S) efficiently, Monte-Carlo simulation and other heuristics have been used in various previous works [1], [5], [7], [6], [3], [12], [8], [7], [16].”
Scalable Influence Maximization in Social Networks under the Linear Threshold Model (2010, methodology): “comparable (as shown in [5]) or slower (as shown in our tests) than the optimized greedy algorithm.”
Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering (2013, neutral): “[44].”