Deep Graph Infomax
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.
 This paper describes an approach for unsupervised learning of node features on a graph (with known structure), so that learned local representations represent community information that has high mutual info with a graph-level summary. The general idea is they apply InfoMax to graphs via graph convolutional networks (GCN), and report impressive results, including rivaling supervised learning methods for node classification. The 3 experiments are on paper topic classification, social network modeling, and protein classification. The idea of using InfoMax with GCNs for unsupervised node learning is clever and timely, the technical contribution is solid, the experiments are executed well, and the paper is clear and easy to read.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Graph Infomax
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper describes an approach for unsupervised learning of node features on a graph (with known structure), so that learned local representations represent community information that has high mutual info with a graph-level summary. The general idea is they apply InfoMax to graphs via graph convolutional networks (GCN), and report impressive results, including rivaling supervised learning methods for node classification. The 3 experiments are on paper topic classification, social network modeling, and protein classification. The idea of using InfoMax with GCNs for unsupervised node learning is clever and timely, the technical contribution is solid, the experiments are executed well, and the paper is clear and easy to read.
assistant
 This paper describes an approach for unsupervised learning of node features on a graph (with known structure), so that learned local representations represent community information that has high mutual info with a graph-level summary. The general idea is they apply InfoMax to graphs via graph convolutional networks (GCN), and report impressive results, including rivaling supervised learning methods for node classification. The 3 experiments are on paper topic classification, social network modeling, and protein classification. The idea of using InfoMax with GCNs for unsupervised node learning is clever and timely, the technical contribution is solid, the experiments are executed well, and the paper is clear and easy to read.