发表机构
Sapienza University of Rome; TIM S.p.A.（罗马第一大学; 意大利电信股份公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于图的推荐在大规模下的效率问题，提出结合图粗化与多步标签传播的两阶段扩散框架，在电信数据集实验中，该方法在可扩展性、延迟和推荐质量间实现平衡，不同阶段采用不同模型有不同提升效果。
AI 中文摘要
基于图的推荐在实际工业应用中广泛采用。然而，这些系统中的图规模巨大，带来可扩展性和效率挑战，需要平衡预测质量和计算成本的技术。本文提出灵活的两阶段扩散框架，结合图粗化与多步标签传播。先应用特定领域启发式方法聚合节点成有意义的社区，减小图规模并保留关键关系。通过标签传播算法或图神经网络进行初始扩散，最后在子图内用第二个标签传播算法生成最终推荐。在真实电信数据集上，两阶段都用标签传播算法时，该方法比全图标签传播算法基线的NDCG@5提高24%，第一阶段加入轻量级图神经网络可将NDCG@5提高超50%，但需大量训练和推理时间。通过实验和消融分析，量化了这些权衡，证明该方法在可扩展性、延迟和推荐质量间实现了最佳平衡。
英文摘要
Graph-based recommendations are widely adopted in real-world industrial applications. However, graphs in these systems often reach a massive scale, posing notable scalability and efficiency challenges. This requires techniques that can effectively balance predictive quality with computational cost. One promising approach is graph coarsening, an adaptive graph reduction technique that offers a way to systematically construct smaller, yet structurally representative, versions of the original large-scale graphs. In this work, we propose a flexible two-stage diffusion framework that combines graph coarsening with multi-step label propagation in the telecommunications domain. Domain-specific heuristics are applied to first aggregate nodes into meaningful communities, reducing graph size while preserving essential business-relevant relationships. An initial diffusion process done by a Label Propagation Algorithm (LPA) or a Graph Neural Network (GNN) propagates labels across the coarsened graph to produce coarse-grained predictions. Finally, a second LPA within subgraphs generates the final recommendations for individual users. On a real-world telecommunications dataset, when using LPA in both stages, our method achieves up to +24% NDCG@5 over the full-graph LPA baseline. Incorporating a lightweight GNN in the first stage further boosts NDCG@5 by more than 50%, but requires substantial training and inference time. Through extensive experiments and a detailed ablation, we quantify these trade-offs and demonstrate that our coarsening-driven approach delivers an optimal balance between scalability, latency, and recommendation quality.
