Skip-graph: Learning graph embeddings with an encoder-decoder model
In this work, we study the problem of feature representation learning for graph-structured data. Many of the existing work in the area are task-specific and based on supervised techniques. We study a method for obtaining a generic feature representation for a graph using an unsupervised approach. The neural encoder-decoder model is a method that has been used in the natural language processing domain to learn feature representations of sentences. In our proposed approach, we train the encoder-decoder model to predict the random walk sequence of neighboring regions in a graph given a random walk along a particular region. The goal is to map subgraphs — as represented by their random walks — that are structurally and functionally similar to nearby locations in feature space. We evaluate the learned graph vectors using several real-world datasets on the graph classification task. The proposed model is able to achieve good results against state-of- the-art techniques.
This paper proposes an unsupervised graph embedding learning method based on random walk and skip-thought model. They show promising results compared to several competitors on four chemical compound datasets. Strength: 1, The idea of learning the graph embedding by applying skip-thought model to random walk sequences is interesting. 2, The paper is well organized. Weakness: 1, As the current datasets are small (e.g., the average number of nodes per graph is around 30), it would be great to explore larger graph datasets to further investigate the method. 2, Comparisons with recent work like LINE and node2vec are missing. You can compare them easily by applying the same aggregation strategy to their node embeddings. Detailed Questions: 1, The description about how to split the random walk sequence into 3 sub-sequences is missing. Also, the line “l_min >= (n_k - 1), … >= l_max” in section 2.2.2 is a mistake. 2, Can you provide the standard deviations of the 5-fold cross validation in Table 2? I’m curious about how stable the algorithm is.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Skip-graph: Learning graph embeddings with an encoder-decoder model
In this work, we study the problem of feature representation learning for graph-structured data. Many of the existing work in the area are task-specific and based on supervised techniques. We study a method for obtaining a generic feature representation for a graph using an unsupervised approach. The neural encoder-decoder model is a method that has been used in the natural language processing domain to learn feature representations of sentences. In our proposed approach, we train the encoder-decoder model to predict the random walk sequence of neighboring regions in a graph given a random walk along a particular region. The goal is to map subgraphs — as represented by their random walks — that are structurally and functionally similar to nearby locations in feature space. We evaluate the learned graph vectors using several real-world datasets on the graph classification task. The proposed model is able to achieve good results against state-of- the-art techniques.
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 proposes an unsupervised graph embedding learning method based on random walk and skip-thought model. They show promising results compared to several competitors on four chemical compound datasets. Strength: 1, The idea of learning the graph embedding by applying skip-thought model to random walk sequences is interesting. 2, The paper is well organized. Weakness: 1, As the current datasets are small (e.g., the average number of nodes per graph is around 30), it would be great to explore larger graph datasets to further investigate the method. 2, Comparisons with recent work like LINE and node2vec are missing. You can compare them easily by applying the same aggregation strategy to their node embeddings. Detailed Questions: 1, The description about how to split the random walk sequence into 3 sub-sequences is missing. Also, the line “l_min >= (n_k - 1), … >= l_max” in section 2.2.2 is a mistake. 2, Can you provide the standard deviations of the 5-fold cross validation in Table 2? I’m curious about how stable the algorithm is.
assistant
This paper proposes an unsupervised graph embedding learning method based on random walk and skip-thought model. They show promising results compared to several competitors on four chemical compound datasets. Strength: 1, The idea of learning the graph embedding by applying skip-thought model to random walk sequences is interesting. 2, The paper is well organized. Weakness: 1, As the current datasets are small (e.g., the average number of nodes per graph is around 30), it would be great to explore larger graph datasets to further investigate the method. 2, Comparisons with recent work like LINE and node2vec are missing. You can compare them easily by applying the same aggregation strategy to their node embeddings. Detailed Questions: 1, The description about how to split the random walk sequence into 3 sub-sequences is missing. Also, the line “l_min >= (n_k - 1), … >= l_max” in section 2.2.2 is a mistake. 2, Can you provide the standard deviations of the 5-fold cross validation in Table 2? I’m curious about how stable the algorithm is.