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.
In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery And Data Mining
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Empirical tests on three real networks show Shapley-value node selection for coverage under reachability rules reaches ~0.9 approximation ratio and beats degree baseline, with one case covering half of Cora using 26 nodes.
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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.