The growing popularity of Online Social Networks (OSN) have prompted an increasing number of companies to promote their brands and products through social media. This paper presents a topological potential scheme for predicting influential nodes from large scale OSNs to support more intelligent brand communication. We first construct a weighted network model for the users and their relationships extracted from the brand-related content in OSNs. We quantitatively measure the individual value of the nodes from both the network structure and brand engagement aspects. Moreover, we have addressed the problem of influence decay along with information propagation in social networks and use the topological potential theory to evaluate the importance of the nodes by their individual values as well as the individual values of their surrounding nodes. The experimental results have shown that the proposed method is able to predict influential nodes in large-scale OSNs. We investigate the top-k influential nodes identified by our method in detail, which are quite different from those identified by using pure network structure or individual value. We can obtain an identification result with a higher ratio of verified users and user coverage by using our method compared to existing typical approaches.

中文翻译：

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TPS：一种预测社交网络智能通信影响力网络节点的拓扑势方案


在线社交网络（OSN）的日益普及促使越来越多的公司通过社交媒体推广其品牌和产品。本文提出了一种拓扑潜力方案，用于从大规模 OSN 中预测有影响力的节点，以支持更智能的品牌传播。我们首先为从 OSN 中的品牌相关内容中提取的用户及其关系构建一个加权网络模型。我们从网络结构和品牌参与度两个方面定量衡量节点的个体价值。此外，我们还解决了社交网络中信息传播影响力衰减的问题，并使用拓扑势理论通过节点的个体值以及周围节点的个体值来评估节点的重要性。实验结果表明，该方法能够预测大规模 OSN 中的影响节点。我们详细研究了我们的方法识别的前k个有影响力的节点，这与使用纯网络结构或个体值识别的节点有很大不同。与现有的典型方法相比，我们的方法可以获得更高的已验证用户比例和用户覆盖率的识别结果。