论文标题
通过动态图神经网络检测有影响力的人
Influencer Detection with Dynamic Graph Neural Networks
论文作者
论文摘要
利用网络信息进行预测任务已成为许多领域中的常见实践。作为目标营销的重要组成部分，有影响力的检测可以从合并动态网络表示中受益。在这项工作中，我们研究了用于影响者检测的不同动态图神经网络（GNN）配置，并使用独特的公司数据集评估其预测性能。我们表明，在GNN中使用深度多头注意并编码时间属性可显着提高性能。此外，我们的经验评估表明，捕获邻里表示比使用网络中心度度量更有益。
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.
