论文标题
基于拓扑特征通过2跳路径的双分链链路预测
Bipartite Link Prediction based on Topological Features via 2-hop Path
论文作者
论文摘要
可以将各种现实世界系统建模为两部分网络。最强大，最简单的链接预测方法之一是线性编码自动编码器（LGAE），它在诸如链接预测和节点群集等挑战性任务上具有有希望的性能。 LGAE依赖于简单的线性模型W.R.T.图形的邻接矩阵学习节点的向量空间表示。在本文中，我们考虑了无法使用节点属性的两分链接预测的情况。使用LGAE时，我们建议将重建的邻接矩阵乘以对称归一化训练邻接矩阵。结果，形成了2个跳跃路径，我们将其用作预测的邻接矩阵来评估模型的性能。合成数据集和现实世界数据集的实验结果表明，我们的方法始终超过图形自动编码器和线性图自动编码器模型中的12个两部分数据集中的10个，并在其他两个两部分数据集中达到了竞争性能。
A variety of real-world systems can be modeled as bipartite networks. One of the most powerful and simple link prediction methods is Linear-Graph Autoencoder(LGAE) which has promising performance on challenging tasks such as link prediction and node clustering. LGAE relies on simple linear model w.r.t. the adjacency matrix of the graph to learn vector space representations of nodes. In this paper, we consider the case of bipartite link predictions where node attributes are unavailable. When using LGAE, we propose to multiply the reconstructed adjacency matrix with a symmetrically normalized training adjacency matrix. As a result, 2-hop paths are formed which we use as the predicted adjacency matrix to evaluate the performance of our model. Experimental results on both synthetic and real-world dataset show our approach consistently outperforms Graph Autoencoder and Linear Graph Autoencoder model in 10 out of 12 bipartite dataset and reaches competitive performances in 2 other bipartite dataset.
