PAC learning of networks from threshold opinion dynamics is efficient when influencers per agent are bounded but computationally hard for majority rules, with a heuristic succeeding in over 98% of simulations.
Joint review of algorithms by Richard Johnsonbaugh and Marcus Schaefer (Pearson/Prentice-Hall, 004) and algorithms by Sanjoy Dasgupta, Christos Papadimitriou and Umesh Vazirani (McGraw-Hill, 008)
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On the Limits of PAC Learning of Networks from Opinion Dynamics
PAC learning of networks from threshold opinion dynamics is efficient when influencers per agent are bounded but computationally hard for majority rules, with a heuristic succeeding in over 98% of simulations.