English(EN) Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search

新的SIMBA框架通过神经代理简化影响力最大化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-11 04:00

研究人员开发了SIMBA，一个用于影响力最大化的新框架，它摒弃了复杂的神经网络和连续优化。SIMBA利用轻量级神经代理和直接离散搜索，结合了均匀锚定的节点嵌入、浅层图神经网络和批量多交换模拟退火。与现有方法相比，这种方法显著减少了计算时间，并提高了影响力传播和数据效率。 AI

影响简化了影响力最大化技术，可能使其在网络分析和推荐系统中得到更广泛的应用。

排序理由该集群包含一篇详细介绍影响力最大化新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Yiqiao Liao, Parinaz Naghizadeh · 2026-08-11 04:00

重新思考基于学习的影响力最大化：简单的神经代理和原生离散搜索

arXiv:2608.08406v1 Announce Type: new 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 framewor…