论文标题
Facebook100网络的基于学习的链接预测分析
Learning-based link prediction analysis for Facebook100 network
论文作者
论文摘要
在社交网络科学中，Facebook是最有趣，最广泛使用的社交网络和媒体平台之一。它的数据有助于社交网络研究的重大演变和链接预测技术，这些技术是链接挖掘和分析中的重要工具。本文对Facebook100网络上的链接预测进行了首次全面分析。我们研究性能并评估不同功能集的多个机器学习算法。为了获得功能，我们使用网络嵌入和基于拓扑的技术，例如Node2Vec和相似性指标的向量。此外，我们还采用了基于节点的功能，这些功能可用于Facebook100网络，但在其他数据集中很少找到。讨论了所采用的方法，并明确提出了结果。最后，我们比较和审查应用模型，其中显示了整体性能和分类率。
In social network science, Facebook is one of the most interesting and widely used social networks and media platforms. Its data contributed to significant evolution of social network research and link prediction techniques, which are important tools in link mining and analysis. This paper gives the first comprehensive analysis of link prediction on the Facebook100 network. We study performance and evaluate multiple machine learning algorithms on different feature sets. To derive features we use network embeddings and topology-based techniques such as node2vec and vectors of similarity metrics. In addition, we also employ node-based features, which are available for Facebook100 network, but rarely found in other datasets. The adopted approaches are discussed and results are clearly presented. Lastly, we compare and review applied models, where overall performance and classification rates are presented.
