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.

本研究解决了时序社交网络中影响力最大化（IM）面临的冷启动问题，创新性地提出了通过影响传播路径（IPP）来识别影响种子的方法。研究表明，利用基于结构的标记方法和张量化的时序图网络（TGN）可以显著提高预测准确性和计算效率，同时通过历史数据增强冷启动节点的新邻居对网络扩展产生积极影响。

冷启动问题下的时序社交网络影响力最大化：一种监督方法