Network of Graph Convolutional Networks Trained on Random Walks
Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data. At the same time, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper we propose a model, Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-GCN model achieves state-of-the-art performance on all of the challenging node classification tasks we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, allowing us to propose N-SAGE, and resilience to adversarial input perturbations.
 The paper proposes a novel graph convolutional network in which a variety of random walk steps are involved with multiple GCNs. The basic idea, introducing long rage dependecy, would be interesting. Robustness for the feature remove is also interesting. The validation set would be important for the proposed method, but for creating larger validation set, labeled training set would become small. How the good balance of training-and-validation can be determined? Discussing choice of the degree would be informative. In introducing many degrees (GCNs) for small labeled nodes semi-supervised setting seems to cause over-fitting.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Network of Graph Convolutional Networks Trained on Random Walks
Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data. At the same time, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper we propose a model, Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-GCN model achieves state-of-the-art performance on all of the challenging node classification tasks we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, allowing us to propose N-SAGE, and resilience to adversarial input perturbations.
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>



 The paper proposes a novel graph convolutional network in which a variety of random walk steps are involved with multiple GCNs. The basic idea, introducing long rage dependecy, would be interesting. Robustness for the feature remove is also interesting. The validation set would be important for the proposed method, but for creating larger validation set, labeled training set would become small. How the good balance of training-and-validation can be determined? Discussing choice of the degree would be informative. In introducing many degrees (GCNs) for small labeled nodes semi-supervised setting seems to cause over-fitting.
assistant
 The paper proposes a novel graph convolutional network in which a variety of random walk steps are involved with multiple GCNs. The basic idea, introducing long rage dependecy, would be interesting. Robustness for the feature remove is also interesting. The validation set would be important for the proposed method, but for creating larger validation set, labeled training set would become small. How the good balance of training-and-validation can be determined? Discussing choice of the degree would be informative. In introducing many degrees (GCNs) for small labeled nodes semi-supervised setting seems to cause over-fitting.