Title: Revolutionizing Graph Aggregation: From Suppression to Amplification via BoostGCN
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Convolutional Networks

Collaborative Filtering

Recommendation System

Graph Aggregation

Information Amplification
Abstract: Graph Convolutional Networks (GCNs) based on linear aggregation  have been widely applied across various domains due to their exceptional performance. To enhance performance, these networks often utilize the graph Laplacian norm to suppress the propagation of information from first-order neighbors. However, this approach may dilute valuable interaction information and make the model slowly learn sparse interaction relationships from neighbors, which increases training time and negatively affects performance. To address these issues, we introduce BoostGCN, a novel linear GCN model that focuses on amplifying significant interactions with first-order neighbors, which enables the model to accurately and quickly capture significant relationships. BoostGCN has relatively fixed parameters, making it user-friendly. Experiments on four real-world datasets demonstrate that BoostGCN outperforms existing state-of-the-art GCN models in both performance and efficiency.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces BoostGCN, a novel Graph Convolutional Network (GCN) model designed to amplify significant interactions among first-order neighbors in recommendation systems, diverging from traditional Laplacian-based suppression methods. The authors argue that existing GCNs overly suppress valuable interaction signals, leading to suboptimal performance and slower learning. BoostGCN employs a logarithmic amplification function to prioritize high-significance interactions, enhancing both efficiency and accuracy. Experiments demonstrate superior performance over state-of-the-art baselines, with notable improvements in training speed (up to 90% faster) and recommendation metrics (e.g., Recall, NDCG). The paper also highlights BoostGCN’s robustness to hyperparameters, popularity debiasing, and noise resistance.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths \n* The shift from suppression to amplification of interactions is a novel and well-motivated contribution, addressing a clear gap in GCN-based recommendation systems.\n* Extensive experiments across diverse datasets validate the model’s superiority in performance and efficiency, with detailed ablation studies (e.g., hyperparameter sensitivity, layer depth).\n* BoostGCN’s fixed parameters and logarithmic scaling make it computationally efficient, achieving significant speedups without sacrificing accuracy.\n\n### Weaknesses\n* While heuristic for $S_{i\\to u}$, the paper lacks formal theoretical analysis of the amplification function’s optimality or convergence guarantees. For example, about why choosing log for amplification.\n* Baselines in Table 1 are all before 2024, while the authors have cited references in 2024 in the paper. More recent baselines are needed.\n* While the author state that the lower for the $\\beta$ will increase the amplification, the ablation on $\\beta$ should cover values lower than $e$. In fact the author should show a 'peak' at the value they finally choose.\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper proposes a measure of significant interactions inspired by previous research, then uses this measure to construct a GCN method that amplifies the significant interactions. The paper demonstrates the effectiveness of the method by conducting various analyses.

STRENGTHS AND WEAKNESSES:
Strength: 
1. The empirical analysis is multi-dimensional, including performance analysis, parameter analysis, noise analysis and so on.
2. The performance is superior to the compared methods on four datasets.

Weaknesses:
1. The paper uses the observation of behavior patterns in user-item interactions from other research as an assumption. The assumption is very restrictive when dealing with more general graphs, which may have a negative impact on the applicability of the proposed method.
2. It is unclear if the proposed method is effective for scenarios other than recommendation.

QUESTIONS:
1. How do you calculate the norm of the set $E^{(0)}$?
2. Using the integer index set $\lbrace 1,2,3,\ldots,n\rbrace$ for $\mathcal{X}_i$ for any i is not proper when the factors depend on $i$; a similar problem for $\hat{\mathcal{X}}_i$
3. Does the method only work for recommendation system?
4. Does it work for non-linear GCN?

LIMITATIONS:
See weaknesses and questions.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper highlights the information suppression problem in GCNs caused by graph Laplacian normalisation, which can dilute important first-order interaction signals, negatively impacting applications such as recommender systems. To overcome this issue, the paper introduces BoostGCN, a linear GCN variant that amplifies significant neighbour interactions through a logarithmic weighting scheme, enabling more effective and efficient representation learning. Significant interactions are identified based on neighbour degree, with higher-degree items assumed to carry stronger signals, motivated by behavioural factors such as trust and conformity, and their influence is selectively amplified during aggregation. Empirical evaluation seems to show that this modification results in a noticeable improvement over baselines.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is well written and easy to follow. It clearly identifies a practical limitation in existing GCNs and provides a simple yet effective solution. The method demonstrates consistent performance and efficiency gains across multiple benchmark datasets. Moreover, the notion of interaction significance used in BoostGCN  is intuitive and aligns with real-world behavioural patterns. \n\nThe paper has following major issues:\n\n - The amplification function is based on empirical intuition, such as using degree as a proxy for significance, without formal justification or theoretical guarantees. \n - The paper does not address dynamic or temporal graphs (as mentioned in Limitations), which are common in recommendation scenarios. But this casts some doubts on the effectiveness of the method in real world settings. \n - The paper lacks theoretical analysis regarding convergence, stability, or generalisation under perturbations.\n - The method is benchmarked only on user–item graphs; it is unclear how well it generalises to other graph domains or tasks. How can we define significance interaction in generic graphs? A discussion on this would be very helpful. \n - The paper overlooks attention-based models such as GAT, which directly address uniform neighbour weighting; the core issue BoostGCN aims to solve. Including a GAT-based baseline (or a discussion of its limitations in this setting) would strengthen the empirical justification for the proposed method.\n\nQUESTIONS:\nI would request the authors to rebut the points raised in the weakness section. If they provide convincing justifications, I will raise my score.\n\nLIMITATIONS:\nWhile the paper includes a brief limitations section focused on the static nature of BoostGCN and its amplification function design, it does not address broader concerns around the societal impacts of recommender systems. In particular, amplification of significant interactions, if tied to popularity or user conformity, could risk reinforcing filter bubbles or popularity bias. A more holistic discussion of potential harms or fairness considerations would improve the completeness and ethical framing of the work.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents the BoostGCN model, aiming to address the issue in traditional Graph Convolutional Networks (GCNs) where graph Laplacian normalization suppresses the information propagation of first - order neighbors. The authors argue that Laplacian normalization dilutes important interaction information, leading to inefficient model learning. BoostGCN enhances the significant interaction information of first - order neighbors through a designed logarithmic amplification function, enabling the model to capture key relationships more quickly and accurately. Experiments on four real - world datasets verify that BoostGCN outperforms existing state - of - the - art models in terms of both performance and efficiency. It also has fixed parameters and strong robustness. The main contributions include shifting the graph aggregation paradigm from “suppression” to “amplification”, designing an interaction saliency amplification function, and validating the model's advantages through experiments.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：​\n1. It breaks through the traditional information suppression idea in GCNs and proposes to enhance the performance of the model by amplifying the significant interactions of first - order neighbors, providing a new research direction for graph aggregation.​\n2. Experiments show that BoostGCN can improve the training time by up to 90% compared with LightGCN. Moreover, with fixed parameters (such as β = e, △^log = 1), it reduces the cost of parameter tuning and is applicable to datasets of different scales and sparsities.​\n3. By comparing with 8 baseline models, conducting experiments on different layers, hyperparameter analysis, and visualization experiments, the effectiveness of the model is proven from multiple perspectives, especially showing advantages in handling long - tailed data and noise.\n\nWeaknesses：​\n1. The quantification of interaction saliency depends on the logarithmic function. However, in the paper, only a heuristic derivation is carried out through the “correlation between interaction quantity and trust/conformity behavior”, lacking strict theoretical proof or mathematical derivation. For example, there is no explanation on why the logarithmic function is superior to other non - linear functions, or whether there is a better - performing amplification function form.​\nRegarding the conclusion that “first - order neighbor interactions are more important”, there is no analysis of its rationality from the theoretical aspects of graph signal processing or representation learning. It only uses the experimental comparison of LightGCN as evidence, resulting in weak theoretical support.​\n2. Currently, only static graph scenarios have been verified, without considering dynamic graph data (such as temporal interactions). It may have limitations when dealing with real - time recommendations or the dynamic evolution of social networks (although it is mentioned in section 4.6, no improvement directions are provided).​\nThe amplification function is based on the assumption that “interaction quantity ≈ saliency”, which may ignore the diversity of user interests (such as unpopular but highly relevant items), and its performance in extremely long - tailed scenarios requires more verification.​\n3. Although it is mentioned that the code will be made public after acceptance, specific implementation details are not provided (such as the specific code logic of the amplification function, the weight calculation method of the weighted layer combination), which may affect the reproducibility of the research by other scholars.​\nIn the dataset pre - processing, it is mentioned that “negative samples are added”, but the sampling strategy for negative samples (such as random sampling, sampling based on popularity) is not clearly defined, which may have an impact on the model performance.\n\nQUESTIONS:\n1. Can the authors supplement the mathematical derivation of the interaction saliency amplification function? For example, prove the advantages of the logarithmic function in balancing “interaction quantity amplification” and “model sensitivity”, or explain through theoretical analysis why the amplification of first - order neighbor information can effectively improve the quality of representation learning. If there are other possible amplification functions (such as the exponential function), it is necessary to compare their performance differences and the reasons for choosing the logarithmic function.​\n2. Regarding the limitations of static graphs mentioned in the paper, do the authors have plans to expand to dynamic graph scenarios? For example, introduce temporal information to adjust the amplification function, or design a mechanism to adapt to the dynamic changes of interactions. If so, please explain the specific ideas; if not, it is necessary to discuss the potential risks of static models in dynamic scenarios.​\n3. The sampling strategy of negative samples in the experiment (such as whether it is based on popularity) is not clear, which may affect the model's ability to recommend unpopular items. In addition, Figure 6 shows that BoostGCN can recommend more non - popular items, but no statistical test (such as the t - test) is used to prove whether the difference from LightGCN is significant. It is recommended to supplement relevant analyses.​\n4. In the paper, β = e is fixed, but the interaction distributions of different datasets may vary greatly (for example, the sparsity of Yelp is much higher than that of MovieLens). Have the authors verified the sensitivity of β on different datasets? For example, whether there are scenarios where β = 2e or 3e performs better. If so, how to automatically adjust β to adapt to different data?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-04"}