GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks only, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. Our method is formulated as a variational autoencoder. We evaluate on the challenging task of conditional molecule generation.
 The authors propose a variational auto encoder architecture to generate graphs. Pros: - the formulation of the problem as the modeling of a probabilistic graph is of interest - some of the main issues with graph generation are acknowledged (e.g. the problem of invariance to node permutation) and a solution is proposed (the binary assignment matrix) - notions for measuring the quality of the output graphs are of interest: here the authors propose some ways to use domain knowledge to check simple properties of molecular graphs Cons: - the work is quite preliminary - many crucial elements in graph generation are not dealt with: a) the adjacency matrix and the label tensors are not independent of each other, the notion of a graph is in itself a way to represent the *relational links* between the various components b) the boundaries between a feasible and an infeasible graph are sharp: one edge or one label can be sufficient for acting the transition independently of the graph size, this makes it a difficult task for a continuous model. The authors acknowledge this but do not offer ways to tackle the issue c) conditioning on the label histogram should make the problem easy: one is giving away the number of nodes and the label identities after all; however even in this setup the approach fails more often than not d) the graph matching procedure proposed is a rough patch for a much deeper problem - the evaluation should include a measure of the capacity of the architecture to : a) reconstruct perfectly the input b) denoise perturbations over node labels and additional/missing edges
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GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks only, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. Our method is formulated as a variational autoencoder. We evaluate on the challenging task of conditional molecule generation.
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<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 authors propose a variational auto encoder architecture to generate graphs. Pros: - the formulation of the problem as the modeling of a probabilistic graph is of interest - some of the main issues with graph generation are acknowledged (e.g. the problem of invariance to node permutation) and a solution is proposed (the binary assignment matrix) - notions for measuring the quality of the output graphs are of interest: here the authors propose some ways to use domain knowledge to check simple properties of molecular graphs Cons: - the work is quite preliminary - many crucial elements in graph generation are not dealt with: a) the adjacency matrix and the label tensors are not independent of each other, the notion of a graph is in itself a way to represent the *relational links* between the various components b) the boundaries between a feasible and an infeasible graph are sharp: one edge or one label can be sufficient for acting the transition independently of the graph size, this makes it a difficult task for a continuous model. The authors acknowledge this but do not offer ways to tackle the issue c) conditioning on the label histogram should make the problem easy: one is giving away the number of nodes and the label identities after all; however even in this setup the approach fails more often than not d) the graph matching procedure proposed is a rough patch for a much deeper problem - the evaluation should include a measure of the capacity of the architecture to : a) reconstruct perfectly the input b) denoise perturbations over node labels and additional/missing edges
assistant
 The authors propose a variational auto encoder architecture to generate graphs. Pros: - the formulation of the problem as the modeling of a probabilistic graph is of interest - some of the main issues with graph generation are acknowledged (e.g. the problem of invariance to node permutation) and a solution is proposed (the binary assignment matrix) - notions for measuring the quality of the output graphs are of interest: here the authors propose some ways to use domain knowledge to check simple properties of molecular graphs Cons: - the work is quite preliminary - many crucial elements in graph generation are not dealt with: a) the adjacency matrix and the label tensors are not independent of each other, the notion of a graph is in itself a way to represent the *relational links* between the various components b) the boundaries between a feasible and an infeasible graph are sharp: one edge or one label can be sufficient for acting the transition independently of the graph size, this makes it a difficult task for a continuous model. The authors acknowledge this but do not offer ways to tackle the issue c) conditioning on the label histogram should make the problem easy: one is giving away the number of nodes and the label identities after all; however even in this setup the approach fails more often than not d) the graph matching procedure proposed is a rough patch for a much deeper problem - the evaluation should include a measure of the capacity of the architecture to : a) reconstruct perfectly the input b) denoise perturbations over node labels and additional/missing edges