Title: CaliGCL: Calibrated Graph Contrastive Learning via Partitioned Similarity and Consistency Discrimination
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Contrastive Learning

Exponential Partitioned Similarity

Semantics-Consistency Discrimination
Abstract: Graph contrastive learning (GCL) aims to learn self-supervised representations by distinguishing positive and negative sample pairs generated from multiple augmented graph views. Despite showing promising performance, GCL still suffers from two critical biases: (1) ***Similarity estimation bias*** arises when feature elements that support positive pair alignment are suppressed by conflicting components within the representation, causing truly positive pairs to appear less similar. (2) ***Semantic shift bias*** occurs when random augmentations alter the underlying semantics of samples, leading to incorrect positive or negative assignments and injecting noise into training. To address these issues, we propose CaliGCL, a GCL model for calibrating the biases by integrating an exponential partitioned similarity measure and a semantics-consistency discriminator. The exponential partitioned similarity computes the similarities among fine-grained partitions obtained through splitting representation vectors and uses exponential scaling to emphasize aligned (positive) partitions while reducing the influence of misaligned (negative) ones. The discriminator dynamically identifies whether augmented sample pairs maintain semantic consistency, enabling correction of misleading contrastive supervision signals. These components jointly reduce biases in similarity estimation and sample pairing, guiding the encoder to learn more robust and semantically meaningful representations. Extensive experiments on multiple benchmarks show that CaliGCL effectively mitigates both types of biases and achieves state-of-the-art performance.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel graph contrastive learning (GCL) model, CaliGCL, which targets at calibrating the similarity estimation bias and the semantic shift bias in the standard GCL framework. Considering this, on one hand, this paper raises an exponential partitioned similarity measure to emphasize the aligned partitions while suppressing conflicting partitions. Besides, the authors also provide theorems to explain the merit of the exponential partitioned similarity measure. On the other hand, this paper designs a semantics-consistency discriminator to assess the semantic relationship between sample pairs, which corrects the contrastive supervision from the semantic shift bias. The model ability is supported through comprehensive experiments on multiple graph datasets against advanced self-supervised graph representation models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n(1) Writing and organization of this paper are good and logical, and the motivation and the method are clear to understand and follow.\n(2) This paper deals with two underexplored bias problems, namely the similarity estimation bias and the semantic shift bias in GCL, and the perspective is interesting.\n(3) This paper proposes the exponential similarity measure and the semantics-consistency discriminator to mitigate the two bias problems, which make sense to calibrate the biases in GCL.\n(4) Experiments are comprehensive, including two downstream tasks across multiple public graph datasets to demonstrate the model superiority.\n\nWeaknesses:\n(1) How to calculate the normalized adjacency matrix $\\tilde{A}$ in Line 186 is missing. The authors need to give the specific mathematical formulation of it.\n(2) Lines 167-168 state that “the proposed exponential partitioned similarity could exceed the standard exponential inner product across all scenarios”. What do “all scenarios” mean? Why do the authors stress the “all scenarios”?\n(3) The authors state that shuffling the feature columns of representations before loss computation could ensure sufficient interactions among feature dimensions across partitions in Lines 220-221. Therefore, it should provide more experimental results to demonstrate the rationality of implementing “Shuffle”. \n(4) The authors need to provide a more comprehensive analysis on complexity when compared with other graph models.\n(5) The experiments in Figure 6 are conducted to evaluate the discriminator, but the legends are about the loss functions. There is a doubt as to which result was caused by applying the discriminator.\n\nQUESTIONS:\n(1) Does the model training converge? Since balancing the training between the encoder and the discriminator may be difficult, the authors should focus more on the loss convergence to ensure stable model training.\n(2) I am curious about whether the block number will cause the index flow? According to Equation 5, a large $K$ will amplify the inner product, which may cause index flow in practice.\n(3) The proposed CaliGCL is compared with some self-supervised graph models with learnable augmentations, which optimize the graph augmentations to improve the model performance. However, the paper still states that these models have the potential to distort semantic consistency between sample pairs. So why do these graph models with learnable augmentations still distort semantic consistency between sample pairs, and what are the advantages of CaliGCL when compared with these models?\n\nLIMITATIONS:\nThe paper has clarified its limitation and the corresponding resolution, and the authors could refine their resolution to address the limitation.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes CaliGCL, a graph model for tackling two biases in graph contrastive learning: similarity estimation bias and semantic shift bias. To alleviate the similarity estimation bias, the authors raise an exponential partitioned similarity metric to emphasize positively-aligned partitions and suppress the negatively-aligned partitions. For eliminating the semantic shift bias, the authors apply a semantics-consistency discriminator to calibrate the sample assignments during the contrastive learning process. Empirical results, such as node classification, graph classification, and ablation study demonstrate the ability of CaliGCL to calibrate the biases when compared with other self-supervised graph contrastive and generative models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- This paper deals with the critical problems in graph contrastive learning, namely the similarity estimation bias and the semantic shift bias, which may cause biased representation learning.\n\n- This paper proposes a novel CaliGCL including the exponential partitioned similarity metric and the semantics-consistency discriminator to tackle the two biases and provides the theoretical evidence to analyze the proposed model.\n\n- Extensive empirical experiments on node classification and graph classification show that the proposed CaliGCL is superior to other comparative self-supervised graph models.\n\nWeaknesses:\n- The function of doubling the inner product of partitions in the proposed exponential partitioned similarity in Eq. (6) is not intuitive. As the paper claims, the exponential partitioned similarity can suppress the impact of negatively-aligned partitions by inducing their exponentials to approach zero while amplifying positively-aligned ones, it is unnecessary to double the inner product of partitions in Eq. (6). The authors need to clarify why the exponential partitioned similarity still implements the double inner product to achieve the amplification and the suppression.\n\n- Figure 3 only provides the mean performance of the proposed model, it would be better to supplement the standard deviation in Figure 3.\n\n- This paper needs visualization analysis to compare the proposed CaliGCL with other graph models, which better shows the embedding distribution and demonstrates the representation quality.\n\nQUESTIONS:\n- What do “connected positives” mean in Figure 2? Do the connected positives mean the adjacent nodes? Besides, where are the encoders in Figure 2?\n\n- Why do the authors use the Hadamard product to fuse representations $\\z_{v_i}$ and $\\z_{v_j}$ but not other fusion strategies like concatenation during the training process of the semantics-consistency discriminator? What is the advantage of the Hadamard product?\n\n- The paper claims that a semantics-consistency discriminator could realize the calibrated sample assignments. Can the authors provide experiment results on whether the discriminator has the ability to identify the positive or negative pairs during training?\n\n- Why don’t the authors sample the negatives and positives with similar sizes when training the discriminator? It can avoid the imbalance problem more efficiently than using a balanced softmax loss.\n\nLIMITATIONS:\nThe authors have provided some plans for refining the limitations as future work, and the plans are reasonable.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper aims to mitigate two problems of graph contrastive learning, \\emph{i.e.,} similarity estimation bias and semantic shift bias, which respectively causes truly positive pairs to appear less similar and leads to incorrect positive or negative assignments as well as training noises. Therefore, the authors developed CaliGCL that is a graph contrastive learning model for calibrating the biases by integrating an exponential partitioned similarity measure and a semantics-consistency discriminator. Extensive experiments are performed to demonstrate the effectiveness and superiority of the work.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1.\tThis paper provides a deep insight into GCL and introduces two problems faced by existing works;\n\n2.\tThe proposed solution of exponential partitioned similarity measure is novel, which divides a sample representation vector into multiple partitions. Exponential function is adopted to emphasize the positive partitions and meanwhile it weakens the negative partitions. This method helps to mitigate the degraded similarity between positive samples caused by similarity estimation bias. The semantics-consistency discriminator also helps to distinguish semantically flipped sample pairs.\n\n3.\tExtensive experiments are performed to demonstrate the effectiveness and superiority of the work.\n\n\nWeaknesses:\n\n1.\tSome concepts should be further specified, like semantic shift bias and contrastive supervision. \n\n2.\tThe figures should be further improved. For example, it seems that the augmentations might possibly flip the nature of sample pairs, but Figure 1 only illustrates that positive pairs might become negative pairs. How about that the negative samples become positive pairs? Similarly, Figure 2 should include more details.\n\n3.\tThe design of discriminator is somehow unclear.\n\nQUESTIONS:\n1.\tWhile pretraining the discriminator, whether the sample pairs confront the problem of random augmentations?\n\n2.\tThe paper mentions that “the partition contrastive objective is an upper bound of mutual information than the original contrastive objective”. Generally, the contrastive objective is the lower bound of mutual information. Please provide a more detailed discussion on this issue.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes CaliGCL, a calibrated contrastive learning framework for graph representation learning. The method adjusts the negative sample distribution in contrastive loss using a soft calibration mechanism based on anchor hardness and positive similarity. Experiments on five benchmarks show that CaliGCL improves performance over various unsupervised and supervised GCL baselines. The authors also provide theoretical insights into calibration effects on similarity distributions.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n\n-The paper targets a relevant and timely problem in GCL, namely the lack of semantic awareness in existing negative sampling strategies.\n\n-The proposed calibration mechanism is conceptually intuitive and can be incorporated into standard contrastive objectives with minimal modification.\n\n-Experimental results show consistent improvements on well-known benchmarks.\n\nWeaknesses：\n\n-Several parts of the theoretical analysis (e.g., Eq. 4, definitions of entropy and hardness) are not clearly explained, making the formal contribution hard to interpret for a broader audience.\n\n-The effect of individual components in the calibration strategy (e.g., hardness vs. positive similarity) is not sufficiently analyzed. This makes it hard to assess where the gains truly come from.\n\n-The paper does not discuss how well the method scales to large graphs or more realistic industrial settings, which limits its practical relevance.\n\n-While the calibration idea is useful, it can be seen as an extension of existing weighting schemes in contrastive learning, with limited theoretical or algorithmic innovation.\n\nQUESTIONS:\n-Could the authors provide a sensitivity analysis regarding the calibration function components (e.g., anchor hardness and positive similarity)? It is unclear how each individually contributes to performance gains.\n\n-The theoretical section, particularly Equation (4) and the definition of entropy-based hardness, lacks clarity. Could the authors elaborate on the derivation and its role in model behavior?\n\n-How does CaliGCL scale in terms of time and memory complexity on large-scale graphs? An empirical or analytical comparison would be helpful to assess real-world applicability.\n\n-How does CaliGCL fundamentally differ from existing adaptive weighting or calibrated contrastive loss approaches in vision or graph domains? Please clarify the novel contribution beyond reweighting strategies.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}