Adaptive Structural Fingerprints for Graph Attention Networks
Many real-world data sets are represented as graphs, such as citation links, social media, and biological interaction. The volatile graph structure makes it non-trivial to employ convolutional neural networks (CNN*s) for graph data processing. Recently, graph attention network (GAT) has proven a promising attempt by combining graph neural networks with attention mechanism, so as to achieve massage passing in graphs with arbitrary structures. However, the attention in GAT is computed mainly based on the similarity between the node content, while the structures of the graph remains largely unemployed (except in masking the attention out of one-hop neighbors). In this paper, we propose an `````````````````````````````*ADaptive Structural Fingerprint* (ADSF) model to fully exploit both topological details of the graph and content features of the nodes. The key idea is to contextualize each node with a weighted, learnable receptive field encoding rich and diverse local graph structures. By doing this, structural interactions between the nodes can be inferred accurately, thus improving subsequent attention layer as well as the convergence of learning. Furthermore, our model provides a useful platform for different subspaces of node features and various scales of graph structures to ``cross-talk** with each other through the learning of multi-head attention, being particularly useful in handling complex real-world data. Encouraging performance is observed on a number of benchmark data sets in node classification.
This paper extends the idea of self-attention in graph NNs, which is typically based on feature similarity between nodes, to include also structural similarity. This is done by computing for each node a value for each other node within its receptive field, calculated based on some distance metric from the node (either a Gaussian decay profile, or a learned weighting of number of hops distance, of based on fixed point of a random walk with restart). When evaluating the attention between two nodes, a function that compares the structural values between the two nodes (based on Jacard similarity) gives a score to the two nodes, which is further used for calculating the attention weight between them. I found the idea to be elegant and well-explained, and overall the paper is well-written. Using the structural similarity makes a lot of sense, and the proposed method is both easy to implement, and flexible — the structural similarity profile can be learned, which seems important for getting this idea to work in practice. I wonder what happens if one uses only the structural similarity for the attention (without the feature similarity). Are there datasets where this would be sufficient? Even a toy task which is constructed such that the structure is more informative than the features could be a nice way to further demonstrate the idea of the paper. The experiments show a clear yet small advantage to the proposed method over the conventional attention method (GAT). Overall, this seems to be a solid contribution (even if the empirical results are a bit incremental) and I recommend acceptance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Adaptive Structural Fingerprints for Graph Attention Networks
Many real-world data sets are represented as graphs, such as citation links, social media, and biological interaction. The volatile graph structure makes it non-trivial to employ convolutional neural networks (CNN*s) for graph data processing. Recently, graph attention network (GAT) has proven a promising attempt by combining graph neural networks with attention mechanism, so as to achieve massage passing in graphs with arbitrary structures. However, the attention in GAT is computed mainly based on the similarity between the node content, while the structures of the graph remains largely unemployed (except in masking the attention out of one-hop neighbors). In this paper, we propose an `````````````````````````````*ADaptive Structural Fingerprint* (ADSF) model to fully exploit both topological details of the graph and content features of the nodes. The key idea is to contextualize each node with a weighted, learnable receptive field encoding rich and diverse local graph structures. By doing this, structural interactions between the nodes can be inferred accurately, thus improving subsequent attention layer as well as the convergence of learning. Furthermore, our model provides a useful platform for different subspaces of node features and various scales of graph structures to ``cross-talk** with each other through the learning of multi-head attention, being particularly useful in handling complex real-world data. Encouraging performance is observed on a number of benchmark data sets in node classification.
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 extends the idea of self-attention in graph NNs, which is typically based on feature similarity between nodes, to include also structural similarity. This is done by computing for each node a value for each other node within its receptive field, calculated based on some distance metric from the node (either a Gaussian decay profile, or a learned weighting of number of hops distance, of based on fixed point of a random walk with restart). When evaluating the attention between two nodes, a function that compares the structural values between the two nodes (based on Jacard similarity) gives a score to the two nodes, which is further used for calculating the attention weight between them. I found the idea to be elegant and well-explained, and overall the paper is well-written. Using the structural similarity makes a lot of sense, and the proposed method is both easy to implement, and flexible — the structural similarity profile can be learned, which seems important for getting this idea to work in practice. I wonder what happens if one uses only the structural similarity for the attention (without the feature similarity). Are there datasets where this would be sufficient? Even a toy task which is constructed such that the structure is more informative than the features could be a nice way to further demonstrate the idea of the paper. The experiments show a clear yet small advantage to the proposed method over the conventional attention method (GAT). Overall, this seems to be a solid contribution (even if the empirical results are a bit incremental) and I recommend acceptance.
assistant
This paper extends the idea of self-attention in graph NNs, which is typically based on feature similarity between nodes, to include also structural similarity. This is done by computing for each node a value for each other node within its receptive field, calculated based on some distance metric from the node (either a Gaussian decay profile, or a learned weighting of number of hops distance, of based on fixed point of a random walk with restart). When evaluating the attention between two nodes, a function that compares the structural values between the two nodes (based on Jacard similarity) gives a score to the two nodes, which is further used for calculating the attention weight between them. I found the idea to be elegant and well-explained, and overall the paper is well-written. Using the structural similarity makes a lot of sense, and the proposed method is both easy to implement, and flexible — the structural similarity profile can be learned, which seems important for getting this idea to work in practice. I wonder what happens if one uses only the structural similarity for the attention (without the feature similarity). Are there datasets where this would be sufficient? Even a toy task which is constructed such that the structure is more informative than the features could be a nice way to further demonstrate the idea of the paper. The experiments show a clear yet small advantage to the proposed method over the conventional attention method (GAT). Overall, this seems to be a solid contribution (even if the empirical results are a bit incremental) and I recommend acceptance.