Leveraging network information for predictive modeling has become widespread
in many domains. Within the realm of referral and targeted marketing,
influencer detection stands out as an area that could greatly benefit from the
incorporation of dynamic network representation due to the ongoing development
of customer-brand relationships. To elaborate this idea, we introduce
INFLECT-DGNN, a new framework for INFLuencer prEdiCTion with Dynamic Graph
Neural Networks that combines Graph Neural Networks (GNN) and Recurrent Neural
Networks (RNN) with weighted loss functions, the Synthetic Minority
Oversampling TEchnique (SMOTE) adapted for graph data, and a carefully crafted
rolling-window strategy. To evaluate predictive performance, we utilize a
unique corporate data set with networks of three cities and derive a
profit-driven evaluation methodology for influencer prediction. Our results
show how using RNN to encode temporal attributes alongside GNNs significantly
improves predictive performance. We compare the results of various models to
demonstrate the importance of capturing graph representation, temporal
dependencies, and using a profit-driven methodology for evaluation.

这篇论文提出了一种基于动态图神经网络的影响力预测框架，并结合了权重损失函数、SMOTE 和滚动窗口策略等方法来改善预测性能；作者使用了独有的企业数据集，其中包含三个城市的网络，并提出了一种基于利润的评估方法，结果表明采用 RNN 编码时序属性可以显著提高预测性能，捕捉图表示形式、时序依赖关系以及使用基于利润的方法进行评估的重要性。

利用动态图神经网络进行影响力预测

INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks

Leveraging network information for prediction tasks has become a common
practice in many domains. Being an important part of targeted marketing,
influencer detection can potentially benefit from incorporating dynamic network
representation. In this work, we investigate different dynamic Graph Neural
Networks (GNNs) configurations for influencer detection and evaluate their
prediction performance using a unique corporate data set. We show that using
deep multi-head attention in GNN and encoding temporal attributes significantly
improves performance. Furthermore, our empirical evaluation illustrates that
capturing neighborhood representation is more beneficial that using network
centrality measures.

本文研究了在影响者检测中，利用动态图神经网络进行预测任务的不同配置，考虑到网络动态性与时间特征属性的影响，证明使用深度多重注意力和编码时间属性可以显著改善性能，在捕获邻域表示方面比使用网络中心度度量更有益。