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User behavior based link prediction in online social networks

机译：在线社交网络中基于用户行为的链接预测

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In recent years, online social networks such as Facebook, Twitter, LinkedIn are most popular visited sites over the internet. Presently, there is a great interest in understanding and studying the relationships among the users in social networks. Existing link prediction methods predicts the links based on the topological structure features and the node attribute features but overlook the benefit other features such as clicks, shares, likes, forwards and comments could bring to any social network. To address this gap we propose a link prediction method based on user actions with the post which includes clicks, shares, likes, forwards and comments. In this paper, we propose link prediction model combining user action metrics and topological structure metrics. The proposed metric can bridge the gap between the existing methods and propose a new metric for defining link prediction. This is a work in progress paper, further as future direction, implementation of the proposed metrics on standard datasets by suitably training the classifiers is the topic of investigation.
机译：近年来，诸如Facebook，Twitter，LinkedIn等在线社交网络是Internet上最受欢迎的访问站点。当前，对理解和研究社交网络中的用户之间的关系非常感兴趣。现有的链接预测方法基于拓扑结构特征和节点属性特征来预测链接，但是忽略了诸如点击，共享，喜欢，转发和评论之类的其他特征可以带给任何社交网络的好处。为了解决这一差距，我们提出了一种基于用户行为的链接预测方法，其中包括点击，分享，喜欢，转发和评论。在本文中，我们提出了结合用户行为指标和拓扑结构指标的链接预测模型。所提出的度量可以弥合现有方法之间的差距，并提出用于定义链路预测的新度量。这是一份进行中的论文，进一步作为未来的方向，通过适当地训练分类器在标准数据集上实现建议的度量标准是研究的主题。

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