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.
Authors take the skip-graph architecture (Kiros 2015) and apply it to classifying labeled graphs (molecular graphs). They do it by creating many sentences by walking the graph randomly, and asking the model to predict previous part and next part from the middle part. Activations of the decoder part of this model on a walk generated from a new graph are used as features for a binary classifier use to predict whether the molecule has anti-cancer properties. Paper is well written, except that evaluation section is missing details of how the embedding is used for actual classification (ie, what classifier is used) Unfortunately I*m not familiar with the dataset and how hard it is to achieve the results they demonstrate, that would be the important factor to weight on the papers acceptance.
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>



Authors take the skip-graph architecture (Kiros 2015) and apply it to classifying labeled graphs (molecular graphs). They do it by creating many sentences by walking the graph randomly, and asking the model to predict previous part and next part from the middle part. Activations of the decoder part of this model on a walk generated from a new graph are used as features for a binary classifier use to predict whether the molecule has anti-cancer properties. Paper is well written, except that evaluation section is missing details of how the embedding is used for actual classification (ie, what classifier is used) Unfortunately I*m not familiar with the dataset and how hard it is to achieve the results they demonstrate, that would be the important factor to weight on the papers acceptance.
assistant
Authors take the skip-graph architecture (Kiros 2015) and apply it to classifying labeled graphs (molecular graphs). They do it by creating many sentences by walking the graph randomly, and asking the model to predict previous part and next part from the middle part. Activations of the decoder part of this model on a walk generated from a new graph are used as features for a binary classifier use to predict whether the molecule has anti-cancer properties. Paper is well written, except that evaluation section is missing details of how the embedding is used for actual classification (ie, what classifier is used) Unfortunately I*m not familiar with the dataset and how hard it is to achieve the results they demonstrate, that would be the important factor to weight on the papers acceptance.