Generalized Label Propagation Methods for Semi-Supervised Learning
The key challenge in semi-supervised learning is how to effectively leverage unlabeled data to improve learning performance. The classical label propagation method, despite its popularity, has limited modeling capability in that it only exploits graph information for making predictions. In this paper, we consider label propagation from a graph signal processing perspective and decompose it into three components: signal, filter, and classifier. By extending the three components, we propose a simple generalized label propagation (GLP) framework for semi-supervised learning. GLP naturally integrates graph and data feature information, and offers the flexibility of selecting appropriate filters and domain-specific classifiers for different applications. Interestingly, GLP also provides new insight into the popular graph convolutional network and elucidates its working mechanisms. Extensive experiments on three citation networks, one knowledge graph, and one image dataset demonstrate the efficiency and effectiveness of GLP.
 This paper gives an overview of the recently proposed techniques for filtering graph signals. Then, the classification performance of several types of graph filters is studied over a few data sets. In my understanding, the main contribution of the paper is an elaboration on the existing graph filtering approaches and different filtering strategies in the problem of classification and studying their links to the recent graph CNNs, rather than the development of a novel and original methodology. Nevertheless, I think that the study might be worth presenting as it is well-written and it offers a nice treatment towards better comprehension of graph filtering techniques in data analysis problems, with convincing performance figures. Some minor comments: - Can you please briefly describe what the classification tasks are in the experiments? - Typos in the sentence above (1) “The objective OF OF”, and in (1): “Laplcacian”
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generalized Label Propagation Methods for Semi-Supervised Learning
The key challenge in semi-supervised learning is how to effectively leverage unlabeled data to improve learning performance. The classical label propagation method, despite its popularity, has limited modeling capability in that it only exploits graph information for making predictions. In this paper, we consider label propagation from a graph signal processing perspective and decompose it into three components: signal, filter, and classifier. By extending the three components, we propose a simple generalized label propagation (GLP) framework for semi-supervised learning. GLP naturally integrates graph and data feature information, and offers the flexibility of selecting appropriate filters and domain-specific classifiers for different applications. Interestingly, GLP also provides new insight into the popular graph convolutional network and elucidates its working mechanisms. Extensive experiments on three citation networks, one knowledge graph, and one image dataset demonstrate the efficiency and effectiveness of GLP.
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 gives an overview of the recently proposed techniques for filtering graph signals. Then, the classification performance of several types of graph filters is studied over a few data sets. In my understanding, the main contribution of the paper is an elaboration on the existing graph filtering approaches and different filtering strategies in the problem of classification and studying their links to the recent graph CNNs, rather than the development of a novel and original methodology. Nevertheless, I think that the study might be worth presenting as it is well-written and it offers a nice treatment towards better comprehension of graph filtering techniques in data analysis problems, with convincing performance figures. Some minor comments: - Can you please briefly describe what the classification tasks are in the experiments? - Typos in the sentence above (1) “The objective OF OF”, and in (1): “Laplcacian”
assistant
 This paper gives an overview of the recently proposed techniques for filtering graph signals. Then, the classification performance of several types of graph filters is studied over a few data sets. In my understanding, the main contribution of the paper is an elaboration on the existing graph filtering approaches and different filtering strategies in the problem of classification and studying their links to the recent graph CNNs, rather than the development of a novel and original methodology. Nevertheless, I think that the study might be worth presenting as it is well-written and it offers a nice treatment towards better comprehension of graph filtering techniques in data analysis problems, with convincing performance figures. Some minor comments: - Can you please briefly describe what the classification tasks are in the experiments? - Typos in the sentence above (1) “The objective OF OF”, and in (1): “Laplcacian”