对偶图表示学习
Dual Graph Representation Learning
摘要：
图表示学习将节点作为低维向量嵌入到大型图中，对许多下游应用都有很大的好处。然而，大多数嵌入框架本质上是传导性的，无法泛化到看不见的节点或学习不同图的表示。虽然归纳方法可以推广到看不见的节点，但它们忽略了节点的不同上下文，并且不能双重学习节点嵌入。在本文中，我们提出了一个上下文感知的无监督双重编码框架，\textbf{CADE}，生成表示的节点相结合的实时邻域与邻居关注的表示，并保留额外的内存已知节点。通过与最先进的方法进行比较，我们证明了我们的方法是有效的。
英文摘要：
Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications. Most embedding frameworks, however, are inherently transductive and unable to generalize to unseen nodes or learn representations across different graphs. Although inductive approaches can generalize to unseen nodes, they neglect different contexts of nodes and cannot learn node embeddings dually. In this paper, we present a context-aware unsupervised dual encoding framework, \textbf{CADE}, to generate representations of nodes by combining real-time neighborhoods with neighbor-attentioned representation, and preserving extra memory of known nodes. We exhibit that our approach is effective by comparing to state-of-the-art methods.
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。
