Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search
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Computer Science > Machine Learning
Title:Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search
Abstract:Existing learning-based influence maximization frameworks rely heavily on complex neural architectures and continuous optimization over seed representations. We challenge this paradigm with SIMBA, a diffusion-model-agnostic framework pairing a lightweight neural surrogate with direct discrete search. SIMBA introduces three key components: 1) uniformly anchored node embeddings that eliminate initialization noise and encourage learning driven by graph topology and diffusion pattern, 2) a shallow two-layer graph neural network surrogate predicting final infection states, and 3) batched multi-swap simulated annealing that explores combinatorial seed space without gradients or continuous relaxation. By shifting compute from complex representation learning to effective discrete search, SIMBA drastically cuts time-to-solution while achieving superior influence spread and data efficiency. Our code is available at this https URL.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.08406 [cs.LG] |
| (or arXiv:2608.08406v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.08406
arXiv-issued DOI via DataCite (pending registration)
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