符号预测的统计链接标签模型：通过连接局部和全局信息来平滑稀疏性
Statistical Link Label Modeling for Sign Prediction: Smoothing Sparsity by Joining Local and Global Information
摘要：
符号网络中的一个主要问题是利用网络结构来预测边的丢失符号。在本文中，我们介绍了一种新的概率方法的符号预测问题。所提出的模型的主要特点是它们能够适应输入网络的稀疏程度。网络的稀疏性通常是许多链接预测算法，特别是符号预测算法性能差的主要原因之一。建立一个模型，有能力适应数据的稀疏性尚未被考虑在以前的相关工作。我们认为，存在着一个局部和全局结构之间的困境，并试图建立稀疏自适应模型，解决这个困境。为此，我们提出了基于局部和全局结构的概率预测模型，并根据平滑的概念将它们整合起来。当稀疏性增加时，该模型更多地依赖于全局结构，而对于低水平的稀疏性，它对从局部结构获得的信息给予更多的权重。在三个真实的签名网络上对该模型进行了评估，实验表明其优于现有方法。与以往的方法相比，该模型不仅更好地处理稀疏性问题，而且具有较低的计算复杂性，并可以使用实时数据流进行更新。
英文摘要：
One of the major issues in signed networks is to use network structure to predict the missing sign of an edge. In this paper, we introduce a novel probabilistic approach for the sign prediction problem. The main characteristic of the proposed models is their ability to adapt to the sparsity level of an input network. The sparsity of networks is one of the major reasons for the poor performance of many link prediction algorithms, in general, and sign prediction algorithms, in particular. Building a model that has an ability to adapt to the sparsity of the data has not yet been considered in the previous related works. We suggest that there exists a dilemma between local and global structures and attempt to build sparsity adaptive models by resolving this dilemma. To this end, we propose probabilistic prediction models based on local and global structures and integrate them based on the concept of smoothing. The model relies more on the global structures when the sparsity increases, whereas it gives more weights to the information obtained from local structures for low levels of the sparsity. The proposed model is assessed on three real-world signed networks, and the experiments reveal its consistent superiority over the state of the art methods. As compared to the previous methods, the proposed model not only better handles the sparsity problem, but also has lower computational complexity and can be updated using real-time data streams.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
