Title: TAMI: Taming Heterogeneity in Temporal Interactions for Temporal Graph Link Prediction
Venue: NeurIPS 2025
Accepted: yes
Keywords: Temporal Graph Neural Network

Interaction Heterogeneity

Skewed Distribution

Time Encoding Function

Link History Aggregation
Abstract: Temporal graph link prediction aims to predict future interactions between nodes in a graph based on their historical interactions, which are encoded in node embeddings. We observe that heterogeneity naturally appears in temporal interactions, e.g., a few node pairs can make most interaction events, and interaction events happen at varying intervals. This leads to the problems of ineffective temporal information encoding and forgetting of past interactions for a pair of nodes that interact intermittently for their link prediction. Existing methods, however, do not consider such heterogeneity in their learning process, and thus their learned temporal node embeddings are less effective, especially when predicting the links for infrequently interacting node pairs. To cope with the heterogeneity, we propose a novel framework called TAMI, which contains two effective components, namely log time encoding function (LTE) and link history aggregation (LHA). LTE better encodes the temporal information through transforming interaction intervals into more balanced ones, and LHA prevents the historical interactions for each target node pair from being forgotten. State-of-the-art temporal graph neural networks can be seamlessly and readily integrated into TAMI to improve their effectiveness. Experiment results on 13 classic datasets and three newest temporal graph benchmark (TGB) datasets show that TAMI consistently improves the link prediction performance of the underlying models in both transductive and inductive settings. Our code is available at https://github.com/Alleinx/TAMI_temporal_graph.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes two enhancements to existing temporal link prediction methods: (1) applying a logarithmic transformation to the temporal difference $\\Delta t$, and (2) storing the most recent $k$ embeddings for each edge instead of only the latest one. Experimental results demonstrate the effectiveness of the proposed approach on selected baselines.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n\n1. The authors propose two enhancements that can be integrated into existing temporal link prediction methods.\n2. Experimental results verify the effectiveness and efficiency of the proposed methods on the selected baselines and datasets.\n\n**Weaknesse**\n\n1. The technical contribution of this paper is limited, as the proposed modifications are trivial and their effects are expected. Specifically, the LTE method introduces a log transformation to the temporal differences—a well-known technique in machine learning to improve the training stability when the feature scales vary. As such, it is unsurprising that this modification improves model performance when the scales of temporal differences vary. Furthermore, the instability of temporal encoding methods is not a novel observation by the authors—it was previously discussed in GraphMixer [4]. Regarding the LHA module, storing more historical embeddings instead of just the most recent one to mitigate information loss is also an intuitive strategy. Similar ideas have been explored in prior work [3], and the reviewer does not find the proposed LHA design to offer significant novelty or technical depth.\n2. The experimental evaluation lacks comprehensiveness and fails to convincingly support the claimed advantages of the proposed method..\n    1. The newest baseline is published on NeurIPS 2023 (DyGFormer). There are lots of newer link prediction methods that are both more efficient and effective than DyGForemer [1,2,3]. The authors are supposed to report the results of TAMI on these methods, like [1], for a fair and thorough evaluation of the proposed TAMI method.\n    2. The efficiency analysis is limited to two small datasets (i.e., UC and EN) and one computationally expensive baseline. To fully evaluate the efficiency of TAMI, the authors are encouraged to report results on larger datasets and more baselines.\n    \n\n[1] Lu X, Sun L, Zhu T, et al. Improving Temporal Link Prediction via Temporal Walk Matrix Projection[C]//The Thirty-eighth Annual Conference on Neural Information Processing Systems.\n\n[2] Tian Y, Qi Y, Guo F. Freedyg: Frequency enhanced continuous-time dynamic graph model for link prediction[C]//The Twelfth International Conference on Learning Representations. 2024.\n\n[3] Cheng K, Linzhi P, Ye J, et al. Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction[C]//Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024: 421-432.\n\n[4] Cong W, Zhang S, Kang J, et al. DO WE REALLY NEED COMPLICATED MODEL ARCHITECTURES FOR TEMPORAL NETWORKS?[C]//11th International Conference on Learning Representations, ICLR 2023. 2023.\n\nQUESTIONS:\nPlease see the weakness part.\n\nLIMITATIONS:\nThe limitations have been provided in Appendix C.3", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the challenge of temporal graph link prediction by identifying heterogeneity in interaction patterns, such as skewed interaction frequencies and irregular time intervals, which hinder effective temporal information encoding and cause forgetting of past interactions. It proposes TAMI, a novel framework with two components: Log Time Encoding, which balances skewed time intervals using logarithmic transformation, and Link History Aggregation, which prevents forgetting historical interactions for node pairs.TAMI integrates seamlessly with existing temporal graph neural networks, enhancing their performance. Extensive experiments on 13 classic datasets and 3 TGB datasets demonstrate that TAMI significantly improves link prediction accuracy and training efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nThe paper is well-organized, providing a clear and comprehensive overview of Temporal Graph Link Prediction. It features precise formula definitions, well-formulated problem hypotheses, rigorous mathematical proofs, and extensive comparative and ablation experiments. The narrative is logical and coherent, ensuring a robust structure. Key strengths include being the first to identify heterogeneity in continuous-time temporal graph interactions and its impact on link prediction performance. The proposed TAMI framework includes Log Time Encoding, which rescales time differences via logarithmic transformation, and Link History Aggregation, which preserves historical interactions for target node pairs. Existing temporal graph neural networks can be seamlessly integrated into TAMI, enhancing their effectiveness.\n\nWeaknesses\nIn Figure 4, when describing historical interactions between node pairs, node v5 appears twice as an interaction node for node u, which may be a typographical error and warrants author clarification. The paper does not discuss limitations or shortcomings of the proposed method. Although the authors conducted thorough experiments, they did not clarify whether the hyperparameter in Equation (4), used to measure the forgetting rate of historical interactions, varies across different datasets; if so, further explanation is needed.\n\nQUESTIONS:\nIn Section 4.6, the authors state that “UC and EN datasets require only 2 and 32 historical interactions for attention calculation,” indicating that the choice of the number of historical interactions (k) varies across datasets. Is this variation related to dataset characteristics such as sparsity, scale, or interaction depth?\n\nLIMITATIONS:\nThe authors’ contributions focus on improving temporal encoding and information aggregation, offering a novel perspective and effectively enhancing existing models. However, the approach lacks methodological innovation, resulting in somewhat limited overall contributions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis manuscript focuses on the problem of link prediction over temporal graphs. The authors first identify the observation that node interactions in temporal graphs are highly skewed in terms of frequency and interval, which negatively impacts the performance of existing temporal link predication methods. To address this issue, a light TAMI framework is proposed, consisting of two components LTE and LHA. LTE alleviates the skewness in node interactions, while LHA addresses the forgetting of historical interactions. Extensive experiments demonstrate that TAMI significantly enhances the performance of TGNNs on link prediction, while also accelerating convergence and reducing GPU memory usage.\n\nSTRENGTHS AND WEAKNESSES:\nThis manuscript has the following strengths.\n\n(S1) The paper is well organized, with clear motivation for the LTE and LHA, effectively supported by Figure 3 and Figure 5, respectively.\n\n(S2) The TAMI framework is light-weight and can be easily implemented and incorporated with various backbone TGNNs.\n\n(S3) TAMI demonstrates effectiveness for both transductive and inductive link prediction.\n\nNevertheless, there are also several weaknesses in this manuscript that should be further improved.\n\n(O1) In Section 3.3, the authors state that LHA uses most-recent aggregation. This effectively renders the parameter k redundant, as only a single historical interaction is considered regardless the value of k. Intuitively, using a larger k should enhance the performance of TAMI by incorporating more historical context. Therefore, additional experiments should be conducted with varying values of k, as well as alternative aggregation functions such as sum, which would make practical use of multiple historical interactions.\n\n(O2) Some experimental results require more detailed interpretation. As shown in Table 11, datasets CO, LA, and SE exhibit higher skewness. However, Table 1 shows that the performance improvement obtained from TAMI on these datasets are relatively modest. Notably, the SE dataset, despite having a skewness over 140, shows less than 2% performance improvement, which is significantly lower than the improvements observed on other datasets. This finding appears to contradict the motivation that TAMI should be especially effective on highly skewed datasets. The authors should provide a deeper explanation for this discrepancy. Besides, the skewness of TGB generated datasets should also be reported.\n\n(O3) Similarly, in Figure 5, the performance improvement achieved by LHA on exclusive nodes for datasets EN and UC is smaller than improvement achieved on isolated and mutual nodes. Intuitively, one would expect LHA to yield the most significant improvement on exclusive nodes. The authors should provide additional insights or analysis to clarify why LHA appears less effective on exclusive nodes in EN and UC compared to the other two datasets.\n\nQUESTIONS:\nI hope the authors could provide a clear response to the weaknesses mentioned above, especially point (O1) and (O2).\n\nLIMITATIONS:\nnone", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses limitations in existing time encoding methods for dynamic graphs by analyzing the statistical distribution of temporal interactions, showing that they follow a Pareto distribution. The authors argue that commonly used sinusoidal time encoding functions are biased towards frequent interactions and fail to capture long-range, infrequent ones effectively. Furthermore, they identify a limitation in the practice of using only the most recent $m$ interactions for node embeddings, which tends to neglect important historical context between node pairs. To address these issues, the paper introduces two key contributions: (1) Log Time Encoding (LTE), which applies a logarithmic transformation to rescale time differences, making temporal patterns easier to learn; and (2) Link History Aggregation (LHA), which preserves a fixed number of most recent interactions per target node pair to ensure rare but relevant past links are retained during link prediction. These design choices are theoretically motivated and empirically evaluated to demonstrate improved performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Extensive experimental setup across several dynamic graph models (e.g., DyGFormer, TGN, etc), datasets, and settings (inductive, transductive).\n2. The proposed LTE module can be easily integrated into any dynamic graph encoder. Experiments show that it almost never hurts performance.\n3. Improvement is more pronounces when increasing the number of negative edges.\n\nWeaknesses:\n1. No experiments on other dynamic graph tasks (e.g., dynamic node classification).\n2. Less improvement in the inductive setting and for balanced datasets where the temporal differences roughly follow a normal distribution.\n3. The LTE and LHA modules have minor improvements for some models (e.g., DyGFormer). No provided intuition as to why this occurs.\n\nQUESTIONS:\n1. [1] has found simple linear time encoding functions to be more effective than the sinusoidal function used in your work. Can the linear time encoding function benefit from your module?\n2. LHA module maintains historical interactions for a target node pair that may be missed when performing temporal node message passing. Why does this approach beat RNN-like methods (e.g., TGN) that can also track very long distance interactions? How does this method compare to DyG2Vec [2] which also uses that past $k$ interactions for a target node pair to build temporal embeddings?\n3. Does the choice of aggregation function for LHA affect performance?\n\n[1] Chung et al. Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning. TMLR\n\n[2] Alomrani et al. DyG2Vec: Efficient Representation Learning for Dynamic Graphs. TMLR\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}