Neural Subgraph Isomorphism Counting
In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Although the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in polynomial time compared to the exponential time of the original NP-complete problem. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting requires more global inference to oversee the whole graph. To tackle this problem, we propose a dynamic intermedium attention memory network (DIAMNet) which augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize different subgraph isomorphisms for the global counting. We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different models. Experimental results show that learning based subgraph isomorphism counting can help reduce the time complexity with acceptable accuracy. Our DIAMNet can further improve existing representation learning models for this more global problem.
This paper proposes a dynamic inter-medium attention memory network and model the sub-graph isomorphism counting problem as a learning problem with both polynomial training and prediction time complexities. Since the testing time is reported in this paper, and the time complexity is one of the main contribution of this paper. The hardware and software used to run the algorithm should be reported in the main article. The author argues that if we use neural networks to learn distributed representations for V_G and V_p or xi_G and xi_P without self-attention, the computational cost will acceptable for large graphs, but the missing of self-attention will hurt the performance. It’s encouraged to do corresponding experiments to compare it with the proposed method and better support the algorithm. One of the main advantages of this paper is that the proposed method can efficiently deal with large graph tasks, so the model behaviors of different models in large dataset similar to Figure 5 is encouraged to be given.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Subgraph Isomorphism Counting
In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Although the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in polynomial time compared to the exponential time of the original NP-complete problem. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting requires more global inference to oversee the whole graph. To tackle this problem, we propose a dynamic intermedium attention memory network (DIAMNet) which augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize different subgraph isomorphisms for the global counting. We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different models. Experimental results show that learning based subgraph isomorphism counting can help reduce the time complexity with acceptable accuracy. Our DIAMNet can further improve existing representation learning models for this more global problem.
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 a dynamic inter-medium attention memory network and model the sub-graph isomorphism counting problem as a learning problem with both polynomial training and prediction time complexities. Since the testing time is reported in this paper, and the time complexity is one of the main contribution of this paper. The hardware and software used to run the algorithm should be reported in the main article. The author argues that if we use neural networks to learn distributed representations for V_G and V_p or xi_G and xi_P without self-attention, the computational cost will acceptable for large graphs, but the missing of self-attention will hurt the performance. It’s encouraged to do corresponding experiments to compare it with the proposed method and better support the algorithm. One of the main advantages of this paper is that the proposed method can efficiently deal with large graph tasks, so the model behaviors of different models in large dataset similar to Figure 5 is encouraged to be given.
assistant
This paper proposes a dynamic inter-medium attention memory network and model the sub-graph isomorphism counting problem as a learning problem with both polynomial training and prediction time complexities. Since the testing time is reported in this paper, and the time complexity is one of the main contribution of this paper. The hardware and software used to run the algorithm should be reported in the main article. The author argues that if we use neural networks to learn distributed representations for V_G and V_p or xi_G and xi_P without self-attention, the computational cost will acceptable for large graphs, but the missing of self-attention will hurt the performance. It’s encouraged to do corresponding experiments to compare it with the proposed method and better support the algorithm. One of the main advantages of this paper is that the proposed method can efficiently deal with large graph tasks, so the model behaviors of different models in large dataset similar to Figure 5 is encouraged to be given.