This study introduces a metric to measure the influence of users and communities on Social Media Networks. The proposed method is a combination of Knowledge Graph and Deep Learning approaches. Particularly, an effective Knowledge Graph is built to represent the interaction activities of users. Besides, an unsupervised deep learning model based on Variational Graph Autoencoder is also constructed to further learn and explore the behavior of users. This model is inspired by conventional Graph Convolutional layers. It is not only able to learn the attribute of users themselves but also enhanced to automatically extract and learn from the relationships among users. The model is robust to unseen data and takes no labeling effort. To ensure the state of the art and fashionable for this work, the dataset is collected by a designed crawling system. The experiments show significant performance and promising results which are competitive and outperforms some well-known Graph-convolutional-based. The proposed approach is applied to build a management system for an influencer marketing campaign, called ADVO system. The ADVO system can detect emerging influencers for a determined brand to run its campaign, and help the brand to manage its campaign. The proposed method is already applied in practice.

本研究引入了一个衡量用户和社区对社交媒体网络影响的指标。所提出的方法是知识图谱和深度学习方法的结合。特别是，构建了一个有效的知识图来表示用户的交互活动。此外，还构建了基于变分图自动编码器的无监督深度学习模型，以进一步学习和探索用户的行为。该模型的灵感来自传统的图卷积层。它不仅可以学习用户自身的属性，还可以增强自动提取和学习用户之间的关系。该模型对看不见的数据具有鲁棒性，并且无需标记工作。为了确保这项工作的先进性和时尚性，数据集由设计的爬行系统收集。实验显示出显着的性能和有希望的结果，这些结果具有竞争力，并且优于一些著名的基于图卷积的结果。所提出的方法用于为影响者营销活动构建一个管理系统，称为 ADVO 系统。ADVO 系统可以为确定的品牌检测新兴影响者来运行其活动，并帮助品牌管理其活动。所提出的方法已经在实践中得到应用。