Graph representation learning embeds nodes in large graphs as low-dimensional vectors and 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. Inductive approaches, such as GraphSAGE, neglect different contexts of nodes and cannot learn node embeddings dually. In this paper, we present a context-aware unsupervised dual encoding framework, CADE, to generate representation of nodes by combining real-time neighborhood structure with neighbor-attentioned representation, and preserving extra memory of known nodes. Experimently, we exhibit that our approach is effective by comparing to state-of-the-art methods.
Strengths: 
1. This paper presents a unified framework CADE for unsupervised graph representation learning, which combining the local neighbors and global information. 
2. Based on GraphSAGE, the module DualENC can include positive pairs simultaneously, capturing the context-aware information.
3. In extensive experiments on Pubmed and PPI for link prediction, they demonstrate that their model beats the state-of-the-arts.
Drawbacks:
1. In this paper, the “Dual” refers to output adjacent nodes embeddings, where the adjacent nodes are collected by random walk. But the “dual” here is inconformity with its intrinsic definition and applications, according to the existing work “Dual-Primal Graph Convolutional Networks”. In other words, this paper lacks novelty, which seems incremental to the existing works. Specifically, the base-encoder is a variant of GraphSAGE plus a global embedding. The bi-attention is a variant of GAT.
2. The paper is not clear and structured. There are some confused logics and inaccurate interpretation. For example, in Introduction section, “Based on the hierarchical framework of GraphSAGE, GAT ….”. In Model section, there exists information redundancy and incoherence. For example, in section 3.2, equations 2-6 are reduplicative that have already appeared in algorithm 2.  Figure 1needs more information to help understand the model. To sum up, the connection and presentation of the four modules are not clear, which makes it difficult to read.
3. The experimental comparison methods and results are not complete. The baseline lacks the optimal method, “Deep Graph Infomax (DGI) ICLR 2019”, on unsupervised node classification. The results are not enough to support the claims (Only two benchmarks are compared).  Some important models on unsupervised graph representation learning are missing, e.g., Deep Graph Infomax(Veličković, 2018).