Inductive Representation Learning on Large Graphs
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.
The authors introduce GraphSAGE, an inductive learning representation learning method for graph-structured data.  Unlike previous transductive methods, GraphSAGE is able to generalize the representation to previously unseen nodes.  The representation is learned through a recursive process that samples from a node's neighborhood and aggregates the results.  GraphSAGE outperforms other popular embedding techniques at three node classification tasks.
Quality:
The quality of the paper is very high.  The framework has several attractive qualities: a representation that is invariant with respect to node index, strong performance at three inductive node classification tasks, and fast training and inference in practice.  The authors include code that they intend to release to the public, which is likely to increase the impact of the work.
Clarity:
The paper is very well-written, well-organized, and enjoyable to read.  
Originality:
The idea is incremental but creative and useful.
Significance:
This work is likely to be impactful on the NIPS community due to the strong results as well as the fast and publicly available implementation.  The work builds on recent developments to offer a tangible improvement to the community.
Overall impression: A high-quality paper that is likely to be of interest to the NIPS community and impactful in the field.  Clear accept.