论文标题
学习图表的深度学习
Deep Learning for Learning Graph Representations
论文作者
论文摘要
采矿图数据已成为计算机科学领域的流行研究主题，鉴于近年来网络数据量增加，在学术界和行业中都进行了广泛的研究。但是，大量网络数据为有效分析带来了巨大的挑战。这激发了图表的出现，该图表将图形映射到低维矢量空间，保持原始图形结构和支撑图推理。关于图表有效表示的调查具有深刻的理论意义和重要的现实意义，因此，我们在图表表示/网络嵌入中介绍了一些基本思想，以及本章中的一些代表性模型。
Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years. However, the huge amount of network data has posed great challenges for efficient analysis. This motivates the advent of graph representation which maps the graph into a low-dimension vector space, keeping original graph structure and supporting graph inference. The investigation on efficient representation of a graph has profound theoretical significance and important realistic meaning, we therefore introduce some basic ideas in graph representation/network embedding as well as some representative models in this chapter.
