论文标题
关于图形退化，表示学习和可伸缩性
About Graph Degeneracy, Representation Learning and Scalability
论文作者
论文摘要
图形或网络是表示具有大量交互的数据的非常方便的方法。最近，图形数据上的机器学习获得了很多吸引力。特别是，顶点分类和缺失的边缘检测具有非常有趣的应用，从药物发现到推荐系统。为了实现此类任务，已经完成了巨大的工作，以学习将节点和边缘嵌入到有限的维矢量空间中的嵌入。此任务称为图表学习。但是，图表的学习技术通常显示出良好的时间和记忆复杂性，从而阻止了它们在业务尺寸图中实时使用。在本文中，我们通过利用图形的变性属性（K核分解）来解决此问题。我们提出了两种利用这种分解的技术，以减少基于步行的图表表示算法的时间和记忆消耗。我们评估了几个学术数据集中提出的技术的嵌入和计算资源质量表达的性能。我们的代码可从https://github.com/sbrandeis/kcore-embedding获得
Graphs or networks are a very convenient way to represent data with lots of interaction. Recently, Machine Learning on Graph data has gained a lot of traction. In particular, vertex classification and missing edge detection have very interesting applications, ranging from drug discovery to recommender systems. To achieve such tasks, tremendous work has been accomplished to learn embedding of nodes and edges into finite-dimension vector spaces. This task is called Graph Representation Learning. However, Graph Representation Learning techniques often display prohibitive time and memory complexities, preventing their use in real-time with business size graphs. In this paper, we address this issue by leveraging a degeneracy property of Graphs - the K-Core Decomposition. We present two techniques taking advantage of this decomposition to reduce the time and memory consumption of walk-based Graph Representation Learning algorithms. We evaluate the performances, expressed in terms of quality of embedding and computational resources, of the proposed techniques on several academic datasets. Our code is available at https://github.com/SBrandeis/kcore-embedding
