Quadratic GCN for graph classification
Graph Convolutional Networks (GCN) have been extensively used to classify nodes in graphs and have been shown to outperform most other node classification methods. However, there are currently few GCN based formalism for graph classification tasks. In this task, graphs of different sizes (e.g. graphs representing different protein structures) belong to different classes, and one attempts to predict the graph class. We here propose a solution combing GCN and methods from knowledge graphs to produce a quadratic GCN (Q-GCN). We extend the GCN formalism by adding a quadratic layer to a standard GCN to classify full graphs. Such a layer produces an output with dimensions independent of the graph node number. This output is then passed through a softmax to classify full graphs. We applied this method to a wide range of graph classification problems and show that such a straightforward formalism outperforms state of the art methods for binary graph classification with or without external input on each graph.
The paper proposes a simple modification to existing graph NN methods for classifying the entire graph of possibly variable size. The problem is of interest. The proposed method is on the other hand fairly simple and straightforward, as shown in Fig.1 and eq(4), as a simple function of the adjacency matrix A that incorporate the entire graph structure. The results are verified on four datasets with promising results. I have a number of concerns with the paper: *The presentation: The paper seems to come out in a rush, with many critical details missing. For example, the main method, which is the main technical contribution, is described within less 1-page length. The figure 1 is also not very helpful in understanding the details of the method. In experiment section, four datasets are presented, where many of them are introduced without any info about the dataset size, graph size, etc. The comparison methods used in Table 1 are also minimal (only 1 method is compared with in each dataset), and many of the methods are decade old. Some of the comparison methods are even without any formal reference so there is literally no precise info about where the methods are exactly from. Overall it is very difficult for an average reader to be fully convinced of the significance and professionalism of the work. **empirical evaluation: As aforementioned, the experiments are not compared with the recent work and detailed info is critically missing. **algorithmic contribution: this is also a question mark.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Quadratic GCN for graph classification
Graph Convolutional Networks (GCN) have been extensively used to classify nodes in graphs and have been shown to outperform most other node classification methods. However, there are currently few GCN based formalism for graph classification tasks. In this task, graphs of different sizes (e.g. graphs representing different protein structures) belong to different classes, and one attempts to predict the graph class. We here propose a solution combing GCN and methods from knowledge graphs to produce a quadratic GCN (Q-GCN). We extend the GCN formalism by adding a quadratic layer to a standard GCN to classify full graphs. Such a layer produces an output with dimensions independent of the graph node number. This output is then passed through a softmax to classify full graphs. We applied this method to a wide range of graph classification problems and show that such a straightforward formalism outperforms state of the art methods for binary graph classification with or without external input on each graph.
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 simple modification to existing graph NN methods for classifying the entire graph of possibly variable size. The problem is of interest. The proposed method is on the other hand fairly simple and straightforward, as shown in Fig.1 and eq(4), as a simple function of the adjacency matrix A that incorporate the entire graph structure. The results are verified on four datasets with promising results. I have a number of concerns with the paper: *The presentation: The paper seems to come out in a rush, with many critical details missing. For example, the main method, which is the main technical contribution, is described within less 1-page length. The figure 1 is also not very helpful in understanding the details of the method. In experiment section, four datasets are presented, where many of them are introduced without any info about the dataset size, graph size, etc. The comparison methods used in Table 1 are also minimal (only 1 method is compared with in each dataset), and many of the methods are decade old. Some of the comparison methods are even without any formal reference so there is literally no precise info about where the methods are exactly from. Overall it is very difficult for an average reader to be fully convinced of the significance and professionalism of the work. **empirical evaluation: As aforementioned, the experiments are not compared with the recent work and detailed info is critically missing. **algorithmic contribution: this is also a question mark.
assistant
The paper proposes a simple modification to existing graph NN methods for classifying the entire graph of possibly variable size. The problem is of interest. The proposed method is on the other hand fairly simple and straightforward, as shown in Fig.1 and eq(4), as a simple function of the adjacency matrix A that incorporate the entire graph structure. The results are verified on four datasets with promising results. I have a number of concerns with the paper: *The presentation: The paper seems to come out in a rush, with many critical details missing. For example, the main method, which is the main technical contribution, is described within less 1-page length. The figure 1 is also not very helpful in understanding the details of the method. In experiment section, four datasets are presented, where many of them are introduced without any info about the dataset size, graph size, etc. The comparison methods used in Table 1 are also minimal (only 1 method is compared with in each dataset), and many of the methods are decade old. Some of the comparison methods are even without any formal reference so there is literally no precise info about where the methods are exactly from. Overall it is very difficult for an average reader to be fully convinced of the significance and professionalism of the work. **empirical evaluation: As aforementioned, the experiments are not compared with the recent work and detailed info is critically missing. **algorithmic contribution: this is also a question mark.