Regional based query in graph active learning
Graph convolution networks (GCN) have emerged as a leading method to classify nodes and graphs. These GCN have been combined with active learning (AL) methods, when a small chosen set of tagged examples can be used. Most AL-GCN use the sample class uncertainty as selection criteria, and not the graph. In contrast, representative sampling uses the graph, but not the prediction. We propose to combine the two and query nodes based on the uncertainty of the graph around them. We here propose two novel methods to select optimal nodes in AL-GCN that explicitly use the graph information to query for optimal nodes. The first method named regional uncertainty is an extension of the classical entropy measure, but instead of sampling nodes with high entropy, we propose to sample nodes surrounded by nodes of different classes, or nodes with high ambiguity. The second method called Adaptive Page-Rank is an extension of the page-rank algorithm, where nodes that have a low probability of being reached by random walks from tagged nodes are selected. We show that the latter is optimal when the fraction of tagged nodes is low, and when this fraction grows to one over the average degree, the regional uncertainty performs better than all existing methods. While we have tested these methods on graphs, such methods can be extended to any classification problem, where a distance can be defined between the input samples.
The authors present an algorithm for actively learning the nodes in a graph that should be sampled/labeled in order to improve classifier performance the most. The proposed techniques use both the graph structure, and the current classifier performance/accuracy into account while (actively) selecting the next node to be labeled. There seem to be two main contributions in the paper. 1) The propose to sample nodes nodes based on *regional* uncertainty rather than node uncertainty 2) They use an variant of pagerank to determine nodes that are central, and hence most likely to affect subsequent classification in graph convolution classifiers. Both approaches seem to be interesting. There are experiments to show effectiveness of these techniques, and there are some interesting observations (for example, that the APR technique works better for smaller sample sizes, while the regional uncertainty methods do better for larger sampling fractions.). While both techniques seem straightforward extensions of previous approaches (and are well explained in the paper), the experiments indicate that they work better than prior approaches. It would have been nice if the authors had also discussed ways in which one or more of these techniques could be combined though, or discussed how we could pick the right approach (in a more empirical way, since it is not clear what the threshold for high sampling rate/low sampling rate distinction is, or if it varies from problem to problem)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Regional based query in graph active learning
Graph convolution networks (GCN) have emerged as a leading method to classify nodes and graphs. These GCN have been combined with active learning (AL) methods, when a small chosen set of tagged examples can be used. Most AL-GCN use the sample class uncertainty as selection criteria, and not the graph. In contrast, representative sampling uses the graph, but not the prediction. We propose to combine the two and query nodes based on the uncertainty of the graph around them. We here propose two novel methods to select optimal nodes in AL-GCN that explicitly use the graph information to query for optimal nodes. The first method named regional uncertainty is an extension of the classical entropy measure, but instead of sampling nodes with high entropy, we propose to sample nodes surrounded by nodes of different classes, or nodes with high ambiguity. The second method called Adaptive Page-Rank is an extension of the page-rank algorithm, where nodes that have a low probability of being reached by random walks from tagged nodes are selected. We show that the latter is optimal when the fraction of tagged nodes is low, and when this fraction grows to one over the average degree, the regional uncertainty performs better than all existing methods. While we have tested these methods on graphs, such methods can be extended to any classification problem, where a distance can be defined between the input samples.
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 authors present an algorithm for actively learning the nodes in a graph that should be sampled/labeled in order to improve classifier performance the most. The proposed techniques use both the graph structure, and the current classifier performance/accuracy into account while (actively) selecting the next node to be labeled. There seem to be two main contributions in the paper. 1) The propose to sample nodes nodes based on *regional* uncertainty rather than node uncertainty 2) They use an variant of pagerank to determine nodes that are central, and hence most likely to affect subsequent classification in graph convolution classifiers. Both approaches seem to be interesting. There are experiments to show effectiveness of these techniques, and there are some interesting observations (for example, that the APR technique works better for smaller sample sizes, while the regional uncertainty methods do better for larger sampling fractions.). While both techniques seem straightforward extensions of previous approaches (and are well explained in the paper), the experiments indicate that they work better than prior approaches. It would have been nice if the authors had also discussed ways in which one or more of these techniques could be combined though, or discussed how we could pick the right approach (in a more empirical way, since it is not clear what the threshold for high sampling rate/low sampling rate distinction is, or if it varies from problem to problem)
assistant
The authors present an algorithm for actively learning the nodes in a graph that should be sampled/labeled in order to improve classifier performance the most. The proposed techniques use both the graph structure, and the current classifier performance/accuracy into account while (actively) selecting the next node to be labeled. There seem to be two main contributions in the paper. 1) The propose to sample nodes nodes based on *regional* uncertainty rather than node uncertainty 2) They use an variant of pagerank to determine nodes that are central, and hence most likely to affect subsequent classification in graph convolution classifiers. Both approaches seem to be interesting. There are experiments to show effectiveness of these techniques, and there are some interesting observations (for example, that the APR technique works better for smaller sample sizes, while the regional uncertainty methods do better for larger sampling fractions.). While both techniques seem straightforward extensions of previous approaches (and are well explained in the paper), the experiments indicate that they work better than prior approaches. It would have been nice if the authors had also discussed ways in which one or more of these techniques could be combined though, or discussed how we could pick the right approach (in a more empirical way, since it is not clear what the threshold for high sampling rate/low sampling rate distinction is, or if it varies from problem to problem)