Link Weight Prediction with Node Embeddings
Application of deep learning has been successful in various domains such as im- age recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present the first generic deep learning approach to the graph link weight prediction problem based on node embeddings. We evaluate this approach with three differ- ent node embedding techniques experimentally and compare its performance with two state-of-the-art non deep learning baseline approaches. Our experiment re- sults suggest that this deep learning approach outperforms the baselines by up to 70% depending on the dataset and embedding technique applied. This approach shows that deep learning can be successfully applied to link weight prediction to improve prediction accuracy.
 Although this paper aims at an interesting and important task, the reviewer does not feel it is ready to be published. Below are some detailed comments: Pros - Numerous public datasets are used for the experiments - Good introductions for some of the existing methods. Cons - The novelty is limited. The basic idea of the proposed method is to simply concatenate the embeddings of two nodes (via activation separately) from both side of edges, which is straightforward and produces only marginal improvement over existing methods (the comparison of Figure 1 and Figure 3 would suggest this fact). The optimization algorithm is not novel either. - Lack of detailed description and analysis for the proposed model S. In Section 5.2, only brief descriptions are given for the proposed approach. - The selected baseline methods are too weak as competitors, some important relevant methods are also missing in the comparisons. For the graph embedding learning task, one of the state-of-the-art approach is conducting Graph Convolutional Networks (GCNs), and GCNs seem to be able to tackle this problem as well. Moreover, the target task of this paper is mathematically identical to the rating prediction problem (if we treat the weight matrix of the graph as the rating matrix, and consider the nodes as users, for example), which can be loved by a classic collaborative filtering solution such as matrix factorization. The authors probably need to survey and compared against the proposed approach.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Link Weight Prediction with Node Embeddings
Application of deep learning has been successful in various domains such as im- age recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present the first generic deep learning approach to the graph link weight prediction problem based on node embeddings. We evaluate this approach with three differ- ent node embedding techniques experimentally and compare its performance with two state-of-the-art non deep learning baseline approaches. Our experiment re- sults suggest that this deep learning approach outperforms the baselines by up to 70% depending on the dataset and embedding technique applied. This approach shows that deep learning can be successfully applied to link weight prediction to improve prediction accuracy.
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>



 Although this paper aims at an interesting and important task, the reviewer does not feel it is ready to be published. Below are some detailed comments: Pros - Numerous public datasets are used for the experiments - Good introductions for some of the existing methods. Cons - The novelty is limited. The basic idea of the proposed method is to simply concatenate the embeddings of two nodes (via activation separately) from both side of edges, which is straightforward and produces only marginal improvement over existing methods (the comparison of Figure 1 and Figure 3 would suggest this fact). The optimization algorithm is not novel either. - Lack of detailed description and analysis for the proposed model S. In Section 5.2, only brief descriptions are given for the proposed approach. - The selected baseline methods are too weak as competitors, some important relevant methods are also missing in the comparisons. For the graph embedding learning task, one of the state-of-the-art approach is conducting Graph Convolutional Networks (GCNs), and GCNs seem to be able to tackle this problem as well. Moreover, the target task of this paper is mathematically identical to the rating prediction problem (if we treat the weight matrix of the graph as the rating matrix, and consider the nodes as users, for example), which can be loved by a classic collaborative filtering solution such as matrix factorization. The authors probably need to survey and compared against the proposed approach.
assistant
 Although this paper aims at an interesting and important task, the reviewer does not feel it is ready to be published. Below are some detailed comments: Pros - Numerous public datasets are used for the experiments - Good introductions for some of the existing methods. Cons - The novelty is limited. The basic idea of the proposed method is to simply concatenate the embeddings of two nodes (via activation separately) from both side of edges, which is straightforward and produces only marginal improvement over existing methods (the comparison of Figure 1 and Figure 3 would suggest this fact). The optimization algorithm is not novel either. - Lack of detailed description and analysis for the proposed model S. In Section 5.2, only brief descriptions are given for the proposed approach. - The selected baseline methods are too weak as competitors, some important relevant methods are also missing in the comparisons. For the graph embedding learning task, one of the state-of-the-art approach is conducting Graph Convolutional Networks (GCNs), and GCNs seem to be able to tackle this problem as well. Moreover, the target task of this paper is mathematically identical to the rating prediction problem (if we treat the weight matrix of the graph as the rating matrix, and consider the nodes as users, for example), which can be loved by a classic collaborative filtering solution such as matrix factorization. The authors probably need to survey and compared against the proposed approach.