Title: Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph few-shot learning

Distribution calibration
Abstract: Graph few-shot learning has attracted increasing attention due to its ability to rapidly adapt models to new tasks with only limited labeled nodes. Despite the remarkable progress made by existing graph few-shot learning methods, several key limitations remain. First, most current approaches rely on predefined and unified graph filters (e.g., low-pass or high-pass filters) to globally enhance or suppress node frequency signals. Such fixed spectral operations fail to account for the heterogeneity of local topological structures inherent in real-world graphs. Moreover, these methods often assume that the support and query sets are drawn from the same distribution. However, under few-shot conditions, the limited labeled data in the support set may not sufficiently capture the complex distribution of the query set, leading to suboptimal generalization. To address these challenges, we propose GRACE, a novel Graph few-shot leaRning framework that integrates Adaptive spectrum experts with Cross-sEt distribution calibration techniques. Theoretically, the proposed approach enhances model generalization by adapting to both local structural variations and cross-set distribution calibration. Empirically, GRACE consistently outperforms state-of-the-art baselines across a wide range of experimental settings. Our code can be found here.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nGRACE gracefully handles hetero/homo-homophilic edges in graph few-shot learning. It consists of two key ingredients: (1) adaptive spectral expert strategy and (2) cross-set distribution calibration strategy. Empirical analysis is done comparing the proposed method to other recent approaches such as Meta-BP. Theoretical results examine the generalization error.\n\nSTRENGTHS AND WEAKNESSES:\nThis is a nicely constructed paper, it clearly outlines challenges in the space of graph FSL and offers intriguing solutions to them. \n\nStrengths: \n1) Quality: To the best of my understanding the choice of baseline comparisons is quite comprehensive and current. The proposed method seems to be well motivated and clearly presented. \n2) Clarity: The paper is written clearly, results are presented clearly. \n3) Originality: Perhaps not groundbreaking, but a nice method that seems well suited to spark interest in those working in this space. \n\nWeaknesses: \n1) Originality: The approach follows a tried and true paradigm of graph FSL, it does not revolutionize things. It is a nice contribution, but leaves the reader thinking if there are things that can be done beyond this structure. \n2) Significance: I do not discount the scientific merit of the paper, however, I do wonder about the use cases of these particular datasets / data types in the presence of todays LLM-universe. It is not clear to me whether the same datasets would be easy or difficult for say a simple prompted LLM. That said this is merely commentary and does not factor into my decision of scoring the paper. \n\nPerhaps relevant: https://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf\n\nQUESTIONS:\nDo you have a hypothesis for why CiteSeer seems to show a different pattern for the ablation in Table 5 than the other datasets? For the other datasets it seems \"w/o cal\" is next best, but on CiteSeer \"w/o high\" is next best?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors argue that real-world graph data often exhibit significant local topological heterogeneity, making the use of a single fixed graph convolutional filter potentially suboptimal. They also highlight that existing baselines typically assume the support and query sets within each task are drawn from the same underlying distribution, which limits their ability to accurately represent the query set under distribution shift conditions.\nTo address these challenges, the authors propose GRACE, a method that employs adaptive spectrum experts leveraging both low-pass and high-pass filters, integrated via a gating mechanism within a Mixture-of-Experts framework to accommodate diverse local connectivity patterns. Additionally, GRACE introduces cross-set distribution calibration, which refines class prototypes using high-density samples to mitigate distributional discrepancies between the support and query sets in a meta-task.\n\nSTRENGTHS AND WEAKNESSES:\n* Strengths\n\n1) The authors propose a simple yet effective method that addresses well-known challenges in the graph domain, namely heterophilic nodes and distribution shift.\n\n2) The authors provide code to promote reproducibility.\n\n* Weakness\n\n1) While the limitation of using a single graph convolutional filter in capturing nodes with diverse connectivity patterns is a well-known issue in the graph domain, it has not been particularly emphasized in the few-shot learning context. The challenge of modeling heterophilic nodes [1] has been widely explored in a separate line of research, independent of few-shot learning. In this case, incorporating convolutional layers that account for structural heterogeneity into existing few-shot learning baselines (e.g., TENT) may already help mitigate the issue.\n\n2) The ablation studies are well conducted, and the case study in Figure 5 shows promising results—demonstrating that the model assigns appropriate weights based on node homophily and calibrates the prototype effectively in the direction of the query sample. However, the analysis would be more complete if the final accuracy were reported with respect to different levels of node homophily, to better clarify the effect of the proposed framework.\nAdditionally, it would be helpful to show the difference in accuracy with and without prototype calibration under varying degrees of distributional shift.\n\n[1] Graph Neural Networks for Graphs with Heterophily: A Survey\n\nQUESTIONS:\n1) Regarding the first weakness I mentioned, could the authors clarify why handling nodes with diverse connectivity patterns is particularly emphasized in the graph few-shot learning context? This issue is a general challenge in graph learning and has been extensively studied in the context of heterophilic graphs. If possible, please include a baseline that combines graph few-shot learning methods with convolutional networks specifically designed for heterophilic graphs.\n\n2) As noted in my second point, while the qualitative results in Figure 5a and 5b are promising, it would strengthen the analysis to include the final accuracy values for those cases. Additionally, please include experiments comparing the accuracy with and without prototype calibration under varying levels of distributional shift to highlight the contribution of this component.\n\n3) As stated in lines 24–29, I agree that in some domains, labeled data may be scarce. However, few-shot learning typically targets generalization to new categories, which differs from the task of learning GNNs with limited labels. Could you clarify the specific scenarios in the graph domain where this meta-learning framework is particularly needed? If possible, it would be helpful to include small-scale experiments on such data (beyond citation networks) to support the motivation.\n\nI am willing to raise my score if all of my concerns are adequately addressed.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles two critical challenges in **graph few-shot learning (GFSL)**:\n\n1. Handling **diverse local structures** in real-world graphs.\n2. Narrowing the **distribution gap** between support and query sets.\n\nThe authors propose a framework GRACE incorporating adaptive spectrum experts and cross-set distribution calibration.  They further validate their claims through theoretical analysis and experiments.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths and Weakness:\n\n#### Strength:\n\n- The paper proposes a novel approach to address the graph few-shot learning task.\n- Theoretical derivations and experimental results are provided to demonstrate the method’s effectiveness.\n\n#### Weakness:\n\nMethodological Concerns.\n\n1.  Prototype Calibration Novelty. The prototype-based strategy in Cross-set distribution calibration lacks originality, as similar concepts have been adopted in methods like PrototypeNet. The authors should offer more clarifications. \n2.  The design of attention mechanism in Equ. (3)  lacks clear motivation and experimental validation. \n3.  The dimension of the node degree matrix D in Equ. (4) may not align with $d$ and is dependent on the number of nodes. How is this issue addressed across datasets with varying node sizes?\n4.  Why use $|\\hat{A}X - X|$ instead of $\\hat{A}X - X$ in Equ. (4) ?\n5.  The $\\hat{\\beta}=0.5(tanh(\\beta) + 1)$ should yield a scalar if $\\beta$ is scalar value, which contradicts the statement in line 184: _$\\hat{\\beta}$ is a trainable parameter_.\n\nQUESTIONS:\n1. The authors primarily selected homophilic datasets. Can additional experiments on heterophilic datasets be provided to support the claim of adaptive handling of heterogeneity?\n\n2. In Table 5, removing the **low-pass expert** (w/o low) yields better performance than removing the **high-pass expert** (w/o high) on 5 homophilic datasets, which contradicts intuition. Theoretically, the low-pass expert should offer more benefits for homophilic datasets, and the authors should provide deeper analysis. \n\n3. The parameter $\\lambda$ in Equ. (2) lacks comprehensive analysis.\n\nLIMITATIONS:\nsee weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes the GRACE framework for graph few-shot learning tasks. The framework introduces adaptive spectrum experts to adapt to the heterogeneity of local topological structures in the graph, and combines the cross-set distribution calibration technology to solve the problem of inconsistent distribution of support sets and query sets, thereby improving the generalization ability of the model. Experiments show that GRACE outperforms existing few-shot learning methods in a variety of experimental settings, verifying its effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper provides a thorough analysis of the issues that need to be addressed in Graph Few-Shot Learning, highlighting the necessity of developing node-specific filtering strategies and narrowing the distribution gap between the support and query sets.\n2. To tackle the aforementioned problems, the paper introduces Adaptive Spectrum Experts and Cross-Set Distribution Calibration.\n3. Experimental results on seven graph datasets, including Cora and CiteSeer, demonstrate the effectiveness of the model. The experiments are comprehensive, and the ablation studies and visual analyses effectively validate the rationale behind the two targeted designs.\n\nWeaknesses:\nThe quality of the paper is relatively high, but there are still some shortcomings:\n1. Areas that need improvement in writing:\n  1) Figure 3 lacks a legend.\n  2) The related work section is missing in the main text, which is quite important and should be included.\n2. There is a lack of discussion on the limitations of the research work in the main text.\n3. The ablation studies are relatively comprehensive, with experiments conducted under various settings on all datasets. Could more experimental settings be considered, such as simultaneously testing the model's performance under 3-shot and 5-shot settings on the DBLP dataset?\n\nQUESTIONS:\nPlease respond to each item in [Weakness] one by one.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}