通过路径标签与图网络提升冷启动节点的影响力预测精度。
Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach
- 用图谱模式标注影响传播路径，提升种子识别效率。
- 冷启动节点通过历史相似路径拓展邻居，提升预测准确率。
- 适用于在线游戏社交网络，适合关注动态关系建模的研究者。
时间图中的影响最大化（IM）旨在识别能推动网络扩展的关键种子节点。我们主张通过影响传播路径（IPPs）定义这些种子，这对大规模网络扩展至关重要。研究重点在于高效标注IPPs并准确预测种子，同时解决时间网络中常被忽视的冷启动问题。提出基于图谱模式的标签方法和针对多关系时间图的张量化时序图网络（TGN），提升了预测准确性和计算效率。此外，通过历史数据中具有相似IPPs的节点为冷启动节点添加新邻居。在在线团队游戏环境中构建了包含弱关系与强关系的多关系时间图，用于实证研究。通过离线实验评估预测精度与训练效率，并结合在线A/B测试验证实际网络增长效果及对冷启动问题的应对能力。
原文摘要 · Abstract (English)
Influence Maximization (IM) in temporal graphs focuses on identifying influential "seeds" that are pivotal for maximizing network expansion. We advocate defining these seeds through Influence Propagation Paths (IPPs), which is essential for scaling up the network. Our focus lies in efficiently labeling IPPs and accurately predicting these seeds, while addressing the often-overlooked cold-start issue prevalent in temporal networks. Our strategy introduces a motif-based labeling method and a tensorized Temporal Graph Network (TGN) tailored for multi-relational temporal graphs, bolstering prediction accuracy and computational efficiency. Moreover, we augment cold-start nodes with new neighbors from historical data sharing similar IPPs. The recommendation system within an online team-based gaming environment presents subtle impact on the social network, forming multi-relational (i.e., weak and strong) temporal graphs for our empirical IM study. We conduct offline experiments to assess prediction accuracy and model training efficiency, complemented by online A/B testing to validate practical network growth and the effectiveness in addressing the cold-start issue.
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