Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search

• URL: http://arxiv.org/abs/2608.08406v1
• Date: Sun, 09 Aug 2026 01:47:51 GMT
• Title: Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search
• Abstract summary: SIMBA is a diffusion-model-agnostic framework pairing a lightweight neural surrogate with direct discrete search.<n>By shifting from complex representation to effective discrete search, SIMBA drastically cuts time-to-solution while achieving superior spread influence and efficiency.
• Score: 5.337915948350182
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 https://github.com/yl489/rethink-IM.

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