图神经网络用于社交网络中的影响力最大化：一种无监督最小支配集方法
Graph Neural Networks for Influence Maximization in Social Networks: An Unsupervised Minimum Dominating Set Approach
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中文总结 AI 辅助
本文提出无监督图神经网络框架求解最小支配集问题，无需真实标签训练，在合成图训练后推理速度比元启发式快55倍，比监督学习快14倍，并能泛化到真实社交网络。
中文摘要 AI 辅助
最小支配集（MDS）问题是经典的NP难组合优化问题，在社交网络分析中具有关键应用，包括病毒式营销、影响力最大化、公共卫生干预和信息传播。识别一组最小的影响力个体，使其覆盖范围遍及整个社交网络，是这些应用的核心，但在大规模场景下仍具有计算挑战性。图神经网络（GNN）已成为在图结构上进行学习的强大工具，近期工作探索了其在困难组合问题上的应用。本文提出了一种新颖的无监督GNN框架用于解决MDS问题，该框架在训练过程中无需真实标签解。我们的方法在12,000个具有多样结构属性的合成图上进行训练，在推理速度上比元启发式基线快达55倍，比监督学习方法快达14倍，同时在真实世界社交网络基准上能够找到最优或接近最优的支配集。我们学习到的启发式方法能够有效泛化到未见过的图分布，展示了在大规模社交网络分析中的强大实际适用性。
英文摘要
The Minimum Dominating Set (MDS) problem is a classic NP-hard combinatorial optimization problem with critical applications in social network analysis, including viral marketing, influence maximization, public health interventions, and information dissemination. Identifying a minimal set of influential individuals whose reach covers an entire social network is central to these applications, yet remains computationally challenging at scale. Graph neural networks (GNNs) have emerged as powerful tools for learning over graphs, and recent work explores their application to hard combinatorial problems. This paper presents a novel unsupervised GNN framework for the MDS problem that eliminates the need for ground-truth solutions during training. Trained on 12,000 synthetic graphs with diverse structural properties, our method achieves up to 55x faster inference than metaheuristic baselines and up to 14x faster inference than supervised learning approaches, while finding optimal or near-optimal dominating sets on real-world social network benchmarks. Our learned heuristic generalizes effectively to unseen graph distributions, demonstrating strong practical applicability for large-scale social network analysis.
发表机构
- Tehran Institute for Advanced Studies (TeIAS)（德黑兰高等研究院）
- University of Tehran（德黑兰大学）
机构由 AI 辅助整理，请以论文原文为准。
