论文标题
双图表示学习
Dual Graph Representation Learning
论文作者
论文摘要
图表学习将节点嵌入大图中，作为低维矢量，对许多下游应用具有很大的好处。但是，大多数嵌入框架本质上是转导的，并且无法概括地看不见的节点或在不同图形上学习表示形式。尽管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，并且无法双重学习节点嵌入。在本文中，我们提出了一个无监督的双重编码框架\ textbf {cade}，以通过将实时社区与邻居注意的表示形式相结合，并保留已知节点的额外记忆来生成节点的表示。我们表明，通过与最先进的方法相比，我们的方法是有效的。
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.
