论文标题
模型：基于图案的深度功能学习链接预测
MODEL: Motif-based Deep Feature Learning for Link Prediction
论文作者
论文摘要
链接预测在网络分析和应用中起着重要作用。最近，链接预测的方法已从传统的基于相似性的算法演变为基于嵌入的算法。但是，大多数现有方法无法利用现实世界网络与随机网络不同的事实。尤其是，已知现实世界网络包含主题，自然网络构建块反映了基础网络生成过程。在本文中，我们提出了一种新颖的嵌入算法，该算法结合了网络图案以捕获网络中的高阶结构。为了评估其对链接预测的有效性，对三种类型的网络进行了实验：社交网络，生物网络和学术网络。结果表明，我们的算法的表现使基于传统的相似性算法的表现均高20％，而基于最新的嵌入算法则优于19％。
Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this paper, we propose a novel embedding algorithm that incorporates network motifs to capture higher-order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms by 20% and the state-of-the-art embedding-based algorithms by 19%.
