用图自动编码器表示网络的异构边模型
Modeling Heterogeneous Edges to Represent Networks with Graph Auto-Encoder
摘要：
在现实世界中，网络中节点之间往往包含多种关系，表现为网络中边的异质性。我们通过使用每个视图来描述节点之间的特定类型的关系，将异构网络转换为多个视图，以便我们可以利用多个视图的协作来学习具有异构边缘的网络的表示。鉴于此，我们提出了一个\emph{regularized graph auto-encoders}（RGAE）模型，致力于利用丰富的信息，在多个视图学习强大的网络表示。更具体地说，RGAE设计了共享和私有图自动编码器作为主要组件，以捕获网络的高阶非线性结构信息。此外，两个损失函数作为正则化，以提取一致和唯一的信息，分别。在真实数据集上的具体实验结果表明，我们的模型在实际应用中优于最先进的基线。
英文摘要：
In the real world, networks often contain multiple relationships among nodes, manifested as the heterogeneity of the edges in the networks. We convert the heterogeneous networks into multiple views by using each view to describe a specific type of relationship between nodes, so that we can leverage the collaboration of multiple views to learn the representation of networks with heterogeneous edges. Given this, we propose a \emph{regularized graph auto-encoders} (RGAE) model, committed to utilizing abundant information in multiple views to learn robust network representations. More specifically, RGAE designs shared and private graph auto-encoders as main components to capture high-order nonlinear structure information of the networks. Besides, two loss functions serve as regularization to extract consistent and unique information, respectively. Concrete experimental results on realistic datasets indicate that our model outperforms state-of-the-art baselines in practical applications.
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。
