基于两跳路径拓扑特征的二分链接预测
Bipartite Link Prediction based on Topological Features via 2-hop Path
摘要：
现实世界中的各种系统都可以建模为二分网络。线性图自动编码器（LGAE）是链接预测中功能最强大、最简单的方法之一，它在链接预测和节点聚类等具有挑战性的任务中具有良好的性能。LGAE依赖于简单的线性模型w.r.t.图的邻接矩阵来学习节点的向量空间表示。在本文中，我们考虑的情况下，二分链接预测的节点属性是不可用的。当使用LGAE时，我们建议将重建的邻接矩阵与对称归一化的训练邻接矩阵相乘。其结果是，2跳路径形成，我们使用的预测邻接矩阵来评估我们的模型的性能。在合成数据集和真实数据集上的实验结果表明，我们的方法在12个二分数据集中的10个数据集上始终优于Graph Autoencoder和Linear Graph Autoencoder模型，并在另外2个二分数据集上达到了竞争性性能。
英文摘要：
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.
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。
