Title: DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
Venue: NeurIPS 2025
Accepted: yes
Keywords: Temporal Graph

Link Prediction

Long-term Sequence Modeling
Abstract: Dynamic graph modeling aims to uncover evolutionary patterns in real-world systems, enabling accurate social recommendation and early detection of cancer cells. Inspired by the success of recent state space models in efficiently capturing long-term dependencies, we propose DyG-Mamba by translating dynamic graph modeling into a long-term sequence modeling problem. Specifically, inspired by Ebbinghaus' forgetting curve, we treat the irregular timespans between events as control signals, allowing DyG-Mamba to dynamically adjust the forgetting of historical information. This mechanism ensures effective usage of irregular timespans, thereby improving both model effectiveness and inductive capability. In addition, inspired by Ebbinghaus' review cycle, we redefine core parameters to ensure that DyG-Mamba selectively reviews historical information and filters out noisy inputs, further enhancing the model’s robustness. Through exhaustive experiments on 12 datasets covering dynamic link prediction and node classification tasks, we show that DyG-Mamba achieves state-of-the-art performance on most datasets, while demonstrating significantly improved computational and memory efficiency. Our code is available at https://github.com/Clearloveyuan/DyG-Mamba.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces DyG-Mamba, a novel continuous state space model (SSM) tailored for dynamic graph modeling. By drawing inspiration from Ebbinghaus forgetting and review cycles, the model introduces a timespan-informed mechanism to dynamically forget or reinforce historical information. DyG-Mamba redefines the core SSM parameters—Δt, A, B, and C—to handle irregular timespans and filter noisy data, thereby aiming to improve the effectiveness, robustness, and efficiency of dynamic graph modeling. The authors evaluate DyG-Mamba on 12 benchmark datasets across tasks like link prediction and node classification, reporting SOTA performance in accuracy, speed, memory, and robustness.\n\nSTRENGTHS AND WEAKNESSES:\n#### Strengths:\n\n1. The paper proposes a timespan-informed continuous SSM for dynamic graph modeling, integrating inspiration from psychological memory theories, which is an intuitive and creative angle. \n2. DyG-Mamba redefines core SSM parameters (∆t, A, B, C) to enable time-aware forgetting and input-conditioned reviewing, aiming to improve robustness and generalization — a technically meaningful contribution. \n3. The model demonstrates strong empirical performance across 12 datasets, with results exceeding or matching state-of-the-art baselines under various negative sampling schemes. \n\n#### Weaknesses:\n1. The metaphorical explanations (e.g., Ebbinghaus forgetting/review curves) are somewhat overemphasized and can feel stretched when mapped to a technical context.\n2. The comparison with recent SSM-based dynamic graph models (e.g., DG-Mamba) is missing, even though such models target similar goals. \n3. While the model is described as lightweight, the added architectural complexity (learnable ∆t function, spectral constraints, dynamic B and C) may increase training difficulty without clear discussion of stability or convergence behavior.\n\nQUESTIONS:\n1. What does the performance look like on smaller graphs with short sequences? Does DyG-Mamba still justify its complexity?\n2. The paper reports linear complexity but test on sequences ≤2,048.  Can DyG-Mamba handle billion-edge graphs (e.g., social networks)?  If not, what are the bottlenecks?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nDyG-Mamba reframes continuous-time dynamic graph modeling as long-sequence modeling with a selective SSM. It treats the irregular timespan between events as a control signal, redefining the SSM’s step size and transition matrix so forgetting follows an Ebbinghaus-style curve. To counteract over-forgetting and noise, it makes the projection parameters input-dependent and applies spectral-norm constraints, letting the model selectively “review” important history.  This design yields linear-time and memory complexity, enabling full-length sequences without pooling.  Across 12 benchmarks in link prediction and node classification, DyG-Mamba attains state-of-the-art accuracy, is up to 8.9× faster, and uses 77 % less GPU memory than Transformer baselines, while remaining robust to up to 60 % noisy edges.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**: \n\n1. The authors reformulate dynamic-graph modeling as a continuous SSM and give formal guarantees on stability, forgetting behaviour (Theorem 4.1), and noise attenuation (Theorem 4.3).\n\n2. The experiments are comprehensive, demonstrating convincing performance compared with various baseline methods.\n\n3. Given the ability of the SSM backbone that scales linearly with sequence length, the proposed method opens the door for much more efficient implementation for many downstream tasks.\n\n**Weakness**:\n\n1. The choice of the specific learnable function for Δt in Equation (11) seems to be important to the method. The paper does not provide a detailed justification or ablation for this specific mathematical form versus other possible monotonically increasing functions. Was this form empirically chosen, and how sensitive is the model to this choice?\n\n2. The paper lacks a detailed discussion on how the theoretical bound in Theorem 4.3 is related to the empirical evaluation in Section 5.4.\n\n3. Experiments remain on modest-scale graphs; it is unclear whether the linear scan kernel remains GPU-efficient on million-edge streams or with heterogeneous edge types.\n\n4. My overall feeling of the paper is to use Mamba architecture on DyGFormer and address the same problem that DyGFormer worked on. Most of the advantages the method exhibits are inherent in the Mamba architecture. Although it is good to see that the authors made improvements on SSM to tailor it for the DGL task, it is still hard to find highlights in the paper.\n\nQUESTIONS:\nNone\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a Mamba-based approach to continuous-time dynamic graphs, with (with mainly) experiments on dynamic link prediction. It is the first state-space model applied to the continuous-graph domain, and the idea is very promising.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n\nThe paper provides a linear-time SSM model, and the idea of applying such methods to continuous-time graphs is both novel and very promising. The design choice of including two SSM blocks, considering both node-level and edge-level information, is also interesting.\n\nThere is solid theoretical depth to the paper, and the connection with Ebbinghaus’ review cycle makes the approach principled and well-motivated.\n\nResults on link-prediction experiments are quite good, and the node-classification challenge is also discussed.\n\nImportantly, the paper introduces an extensive experimental set-up, which demonstrates that the DyG-Mamba structure significantly improves computational and memory efficiency compared with the Transformer.\n\nThe paper studies multiple negative-sampling strategies, adding value and completeness.\n\nThe case study offers valuable insights regarding the comparison with the DyG-Former.\n\n**Weaknesses**\n\nThe central idea is that, for dynamic graphs, especially those in the arduous continuous-time category, a model should learn by examining the long history of the sequence, which can now be extended because the model’s memory requirements do not need extra aggregation. Looking at Figure 2, panel (A) shows at best a marginal, or not even substantial, performance increase, while the message in panel (B) is much clearer. It is unclear what happens with the other networks; one could argue that, for some datasets, relying on a long history is unnecessary. As I see it, the model is not able to distinguish between these two cases.\n\nThe introduction of Δt, connected to the Ebbinghaus curve, is an elegant way to model the decay of historical impact with all its attractive theoretical properties. This choice, however, has the drawback that the decay does not rely on the actual content of the sequence: if an important signal is temporally distant, it could be disregarded. This raises the question, or necessity, of additional gating functions.\n\nAlthough this limitation is acknowledged in the paper, the networks included are limited in both size and domain, making some of the main claims about long-history modelling not easily generalisable from the current experimental set-up.\n\nQUESTIONS:\nThe performance increase appears marginal; why is this the case? More discussion should be added to explore this phenomenon.\n\nCan't also the DyG-Former learn long-term dependencies by using a larger patch size?\n\nHow does the model behave when applied to high-frequency temporal networks?\n\nSome work in the continuous-time graph literature, for example, the Piecewise-Velocity Model for Learning Continuous-Time Dynamic Node Representations [1], is missing from the related-work section. Could it be connected to the proposed SSM (especially as an first-order SSM )?\n\n[1] Piecewise-Velocity Model for Learning Continuous-Time Dynamic Node Representations.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes DyG-Mamba, a new approach for analyzing dynamic graphs. The method adapts state space models by using time gaps between events as control signals. It draws inspiration from Ebbinghaus' forgetting curve, which shows that people forget things quickly at first, then more slowly over time. The model works similarly by adjusting how much it forgets based on time intervals. It also learns to keep important past information while filtering out noise. The main benefit is speed and memory efficiency. DyG-Mamba achieves top results with linear complexity instead of quadratic, making it much faster than transformer-based methods. It also stays robust even when there's a lot of noise in the data. This efficiency makes it possible to handle much larger and longer graph sequences, which could be useful for social networks and recommendation systems.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. This paper introduces the first use of state space models for continuous-time dynamic graphs, which is a solid contribution. The approach of using time gaps as control signals is clever and well-motivated by psychological research on memory. The three theorems provide theoretical backing for the design choices, and the linear complexity is a clear advantage over transformer methods.\n\n2. The experiments cover 12 datasets with multiple evaluation settings, which is thorough. The results are generally strong across different tasks and show good consistency. The efficiency gains are substantial - much faster training and lower memory usage compared to existing methods.\n\n3. The model handles noisy data well, maintaining performance even with 60% noise injection. This is important for real applications where data quality varies. The ablation studies help understand which components matter most.\n\n4. The paper is well-written overall. The connection between memory theories and the technical approach is explained clearly, and the figures help illustrate the main ideas.\n\nWeaknesses:\n\n1. The biggest limitation is that experiments only cover edge addition on relatively small graphs. Real applications involve much larger networks with node changes, feature evolution, and different graph types. The largest dataset has only 2.4M edges, which is small by modern standards.\n\n2. While the linear complexity sounds good, the actual tests only go up to sequence length 2048. It's unclear how the method performs on much longer sequences or whether memory requirements become problematic. The constant factors in the complexity could be large.\n\n3. Some baselines are dated, and newer graph ODE methods are missing. Results for some methods are copied from other papers rather than re-implemented, which raises questions about fair comparison. The evaluation doesn't include recent 2024 methods.\n\n4. The case study doesn't provide much insight into what the model actually learns. It's hard to understand when the method might fail or how it handles changes in data patterns over time. More analysis of the learned representations would be helpful.\n\n5. The paper lacks discussion of training stability, hyperparameter sensitivity, and deployment considerations. The evaluation is limited to link prediction and node classification, missing other important temporal tasks like anomaly detection or community tracking.\n\nQUESTIONS:\n1. The paper only looks at adding edges, but real graphs are more complex - nodes appear and disappear, features change over time. How would you extend DyG-Mamba to handle these cases? Are there basic limitations with using SSMs for node changes?\n2. Your biggest dataset has 2.4M edges, but real networks like Facebook or Twitter are much larger. Do you have any evidence this actually works on bigger graphs? Even tests on fake large graphs would help. The linear complexity sounds good but the hidden constants might be huge.\n3. Why use spectral norm constraints of exactly 1.0? What happens if you change or remove them? The ablation study doesn't really test this. Some basic sensitivity tests would show if this choice actually matters.\n4. The case study is too simple. Can you show clearer examples of what the model chooses to remember versus forget? How does it handle when the data patterns change over time?\n5. Why no comparison with Graph Neural ODEs that also work with continuous time? Also, copying results from other papers instead of running the methods yourself makes it hard to trust the comparisons.\n\n**Main concerns:** The core idea seems reasonable but needs more proof. I'd want to see: (a) tests on realistic graph sizes, (b) better analysis of design choices, (c) clearer understanding of what the model actually does. Right now the scope feels too narrow to be convincing.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}