  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.
