A lightweight GNN surrogate trained on seed-outcome pairs, combined with batched multi-swap simulated annealing, outperforms deeper learning-based influence maximization frameworks on tested benchmarks.
Online processing algorithms for influence maximization
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Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search
A lightweight GNN surrogate trained on seed-outcome pairs, combined with batched multi-swap simulated annealing, outperforms deeper learning-based influence maximization frameworks on tested benchmarks.