A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
摘要
评审与讨论
This paper presents S2GCSL (Simple yet Scalable Granger Causal Structural Learning), a novel method for learning Granger causal graphs from topological event sequences in telecommunication networks. The authors present a simple and scalable method that uses a linear kernel to model activation interactions between event types, and employ gradient descent to efficiently optimize the likelihood function. The method has the ability to incorporate expert knowledge as constraints during optimization, and is with high interpretability. This paper addresses the limitations of existing methods that either ignore topological relationships between devices or lack scalability. The effectiveness, efficiency, and scalability are verified by experiments on both synthetic and real-world telecommunication network fault diagnosis problems. The method can learn causal structures from large-scale alarm data, help identifying root causes of faults in complex telecommunication networks.
优点
The authors present a novel method for Granger causal structural learning tailored specifically for TNFD. The novelty is manifested through innovative problem formulation that incorporates topological event sequences and integrates expert knowledge as constraints to enhance interpretability.
The method combining linear kernels with gradient descent optimization is solid. The experiments are sensible and supportive. The writing is overall good.
The most surprising thing to me is, this paper offers a scalable and efficient solution to a important real-world problem, with potential applications extending beyond telecommunications to other complex networked systems. I am excited to see that a causal learning method can be properly used in a practical scenario.
缺点
The S2GCSL is implemented on batch data. In practical telecommunication network, it is possibly required that fault diagnosis and causal learning are implemented in a real-time fashion. It will be better if the authors can discuss the ability of the proposed method to enable real-time processing and decision-making. For instance, consider an online learning setting instead that continuously updates the causal model as new data arrives could significantly enhance its practical ability.
Besides, in real-world problems, it is possible that there are some missing entries or noise. I suggest the authors discuss how S2GCSL can handle such cases.
typos: Abstract: "Recent years have seen a growing focus on a causal approach to..." -> "Recent years have seen a growing focus on causal approaches to"
Line 47: "...relatively inefficiency" -> "...relative inefficiency"
问题
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As pointed by Weakness 1 and Weakness 2, how can S2GCSL be used in practical online setting where data arrives continuously?
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The authors claim that they could learn Granger causality efficiently on large-scale problems. Could the authors provide some clues about the scale range of problems in real-world scenarios? There are 38 nodes in the real-world dataset used in the experiments.
局限性
No.
Dear Reviewer,
Thank you for your detailed and thoughtful review of our paper. We greatly appreciate your positive evaluation and the time you have taken to provide us with constructive feedback. Below are our responses to your comments:
Weaknesses: 1.Real-Time Granger Causal Discovery: You have raised a crucial point regarding the importance and challenge of real-time Granger causal discovery. While our current method focuses on batch data processing, the natural temporal attribute of event sequences allows our method to be adapted to real-time scenarios. Implementing real-time processing would involve additional considerations, such as handling small sample sizes inherent in streaming data.
2.Handling Missing Entries and Noise: We acknowledge that real-world data often includes missing entries and noise. Addressing these issues is essential for practical applicability. In future work, we plan to develop methods to handle these challenges, ensuring our approach remains robust and reliable under realistic conditions.
3.Writing and Formatting Improvements: We appreciate your patience and the effort you have put into pointing out the typos and formatting inconsistencies. We will correct each of these issues and ensure that the entire paper adheres to a consistent style guide, improving the overall quality of our writing.
Questions: 1.Adaptation to Online Settings: As discussed in the weaknesses, our method can naively be applied to streaming data due to the inherent temporal nature of event sequences. However, adapting the method to better handle the unique challenges of streaming data, such as evolving data distributions and limited data samples, could further enhance its effectiveness in real-time settings.
2.Scale Range of Real-World Problems: Based on our knowledge, the scale of real-world telecommunication network problems typically involves around 30-50 nodes. This scale is reflective of the complexity encountered in practical scenarios, and our method has been validated to perform efficiently within this range.
Thank you again for your high evaluation and valuable feedback. Your insights will help us refine our work and ensure it meets the highest standards of clarity, rigor, and practical applicability.
Thank you for your thorough response. I will keep my positive score.
Thanks for your positive feedback and for recognizing the contribution of our work. We are pleased to incorporate all suggestions into the revised version based on your comments. If you have any further questions, please feel free to bring them up and we will address them as soon as possible. Thanks again.
The paper presents S2GCSL, a scalable and efficient method for Granger causal structural learning from topological event sequences, specifically designed for telecommunication network fault diagnosis. The approach uses a linear kernel to model interactions and employs gradient descent for optimization, incorporating expert knowledge as constraints to enhance interpretability. Extensive experiments on synthetic and real-world datasets demonstrate the method's superior performance in scalability and efficacy compared to existing methods.
优点
The paper demonstrates substantial strengths across multiple dimensions: originality, quality, clarity, and significance.
It introduces an original approach, S2GCSL, for Granger causal structural learning tailored for TNFD, effectively addressing the unique challenges posed by topological event sequences. This originality stems from the novel problem formulation that integrates topological relationships and expert knowledge into the causal learning process, distinguishing it from traditional i.i.d. assumptions.
The quality of the work is underscored by rigorous methodology and extensive experiments, which validate the scalability and efficiency of the proposed method on both synthetic and real-world datasets.
The clarity of the paper is commendable, with well-organized sections and easy-to-follow explanations of complex concepts, although there is some room for improvement in detailing certain methodological aspects and transitions between sections.
The significance of the research is substantial, offering a robust and interpretable solution to a critical real-world problem, with potential applications beyond telecommunications to other complex networked systems such as power grids and transportation networks. By addressing the limitations of existing methods, the paper makes a noteworthy contribution to the field of causal inference and network fault diagnosis.
缺点
Some of the assumptions in the paper may be a little bit strong. Firstly, the paper assumes that the counting processes within each time interval follow a Poisson process. This assumption may not hold in all real-world scenarios, especially in cases with over-dispersed data or memory effects. The authors should explore the robustness of their method to violations of this assumption and consider discussing potential adaptations or extensions that can handle more general or alternative event processes, such as over-dispersed Poisson or negative binomial distributions.
Secondly, the S2GCSL method currently assumes stationarity in the causal relationships. In real-world networks, causal relationships may change over time. The authors should address this limitation by proposing potential extensions or methodologies for adapting the model to dynamic environments. For instance, incorporating online learning techniques or adaptive methods that can update the causal structure as new data arrives could significantly enhance the practical utility of the approach.
Besides, comparing the results on synthetic data and metropolitan telecommunication network alarm data, it shows that S2GCSL suffers a performance drop when transitioning from synthetic to real-world cases. It would be beneficial for the authors to provide a more detailed analysis of this phenomenon.
问题
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The paper assumes that the counting processes within each time interval follow a Poisson process. How would the S2GCSL method perform if the underlying data generation process deviates from the Poisson assumption, for example, if the data exhibit over-dispersion or if the event occurrences are not memoryless?
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The proposed method assumes a stationary environment for modeling the Granger causality. How does the S2GCSL approach handle non-stationarity in the event sequences, where the underlying causal relationships might change over time? Are there mechanisms in place to adapt to such changes dynamically?
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The synthetic data is generated under specific assumptions. How well do these synthetic datasets represent real-world cases? What gaps still exist between the synthetic data generation process and real-world scenarios?
局限性
None
Dear Reviewer,
Thank you for your thorough and insightful review of our paper. We appreciate your positive evaluation and constructive feedback. Here are our responses to your comments:
Weaknesses: 1.Assumption of Poisson Processes: Thank you for highlighting this point. It is indeed a valuable consideration. In the field of TNFD, the assumption that events within each time interval follow a Poisson distribution is widely adopted. This assumption, while standard, is not overly restrictive. Nevertheless, we acknowledge the importance of exploring the robustness of S2GCSL to potential deviations from the Poisson assumption. Future work could extend our approach to handle more general event processes, such as over-dispersed Poisson or negative binomial distributions, to better accommodate various real-world scenarios.
2.Assumption of Stationarity: We appreciate your insightful observation. The assumption of stationarity is indeed common in the domain of Granger causal discovery. However, we recognize that this assumption might not hold in complex real-world scenarios. Adaptation to dynamic environments where causal relationships change over time is a significant challenge, and demand more advanced mathematical tools and methodologies, such as online learning techniques or adaptive algorithms, to dynamically update the causal structure as new data becomes available, thereby enhancing the practical utility of S2GCSL.
3.Performance Drop from Synthetic to Real-World Data: This is an excellent observation. Real-world data indeed presents challenges such as significant noise, sparse causal relationships, and local causality, which are less pronounced in synthetic datasets. These factors contribute to the performance drop, and addressing them is a priority for designing more robust methods to handle these complexities.
Questions: 1.Deviation from Poisson Assumption: The Poisson assumption is a broadly accepted one in the TNFD field. In our specific problem domain, we have not observed significant instances of over-dispersion or non-memoryless event occurrences. Nonetheless, evaluating the performance of S2GCSL under such deviations is an important direction. We acknowledge that addressing scenarios with over-dispersed data or non-memoryless events could provide additional insights into the robustness of our method.
2.Handling Non-Stationarity: This is an excellent question. Currently, the S2GCSL method does not inherently handle non-stationarity. To adapt to non-stationary environments, it might be necessary to move beyond traditional Granger causality and develop techniques to identify and model true causal relationships for stable inference.
3.Representativeness of Synthetic Data: The synthetic data generation method we used is standard in this field, as employed in TMHP and TNPAR. However, we acknowledge the gap between synthetic and real-world data, particularly regarding noise, sparse causal relationships, and local causality. We aim to bridge this gap by further refining our methods to better handle these challenges.
We appreciate the opportunity to improve our paper based on your feedback. The suggested additions and clarifications will enhance the comprehensiveness and applicability of our work. Thank you for your time and valuable suggestions.
This paper presents S2GCSL, a novel method designed to efficiently identify the root causes of alarms in telecommunication networks by learning Granger causal graphs from topological event sequences. This method uses a linear kernel and gradient descent optimization, while incorporating expert knowledge as constraints to enhance interpretability. Extensive experiments on both synthetic and real-world datasets demonstrate that S2GCSL is highly scalable and effective, outperforming existing methods in terms of efficiency and robustness. The primary contributions include the introduction of a scalable causal learning approach, the integration of expert knowledge, and comprehensive validation of the method's applicability in real-world scenarios.
优点
This paper demonstrates strengths across multiple dimensions. Its originality lies in the novel formulation of Granger causal discovery for non-i.i.d. topological event sequences, the creative combination of a linear kernel with gradient-based optimization, and the innovative integration of expert knowledge as constraints.
The quality is evident in its solid theoretical foundation, comprehensive experimentation on both synthetic and real-world data, and thorough comparative analysis against state-of-the-art methods.
The paper's clarity shines through its well-structured presentation, clear problem definition, step-by-step methodology explanation, and effective use of visual aids.
The significance of the work is substantial, addressing critical scalability issues in analyzing large-scale telecommunication networks, offering practical applicability through the incorporation of domain knowledge, and potentially impacting broader fields involving topological event sequences.
By bridging the gap between theoretical Granger causality analysis and practical fault diagnosis in complex networks, S2GCSL represents a noteworthy advancement in the field, with implications for both research and real-world applications in network analysis and fault diagnosis.
缺点
The performance of the S2GCSL method relies on several hyperparameters, such as the geodesic distance k, regularization coefficients \lambda_1 and \lambda_2, and the pruning threshold \rho. The paper should provide more detailed guidelines or heuristics for tuning these hyperparameters in various network settings.
While integrating expert knowledge as constraints is a strength, the paper does not thoroughly discuss the potential impacts of incomplete or inaccurate expert knowledge. For example, dense causal graph.
The writing should be improved, some minor issues: Improve transitions between sections for better flow. For instance, adding a brief summary at the end of the "Related Work" section could help readers transition more smoothly to the "Proposed Method" section.
Ensure all references are formatted according to the same style guide. For instance, "[Granger and CWJ, 2001]" seems a little wired.
Keep consistent with the use of Fig., Figure, Table through the paper. For example, use abbreviation Fig. In the text but Table not abbreviated, and Figure in the caption. Questions
问题
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The choice of hyperparameters like the geodesic distance k, regularization coefficients \lambda_1 and \lambda_2, and the pruning threshold \rho are crucial for the performance of the method. I assume you have some techniques for adjusting these parameters, could you briefly explain them?
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The paper integrates several expert knowledges into the optimization process. However, if the expert knowledge is not the case, e.g., for the sparsity constraints, what is the graph density applied in the synthetic experiments? What if the actual graph is not sparse, how to modify S2GCSL to adapt the situation?
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I notice a phenomenon: In Table 1, as the problem scale increases, the efficiency of ADM4 gradually surpassed that of S2GCSL, but it fell behind again for the largest 50 and 100 node problems. Have you analyzed why this might be the case?
局限性
N/A
Dear Reviewer,
Thank you for your thorough and insightful review of our paper. We appreciate your positive evaluation and the time you have invested in providing such detailed feedback. Below are our responses to your comments:
Weaknesses: 1.Hyperparameter Tuning: Thank you for raising this important point. We will provide more detailed guidelines for tuning hyperparameters in our discussion of the first question below.
2.Impact of Incomplete or Inaccurate Expert Knowledge: This is a valuable suggestion. We will address this concern in our response to the second question below.
3.Writing and Formatting Improvements: We appreciate your patience and detailed suggestions regarding the writing and formatting issues. We will address each typo and ensure all references and terms are consistently formatted. We will also enhance transitions between sections to improve the overall flow of the paper.
Questions: 1.Hyperparameter Tuning: We have indeed developed some techniques for adjusting these hyperparameters: -Geodesic Distance k: We typically set k=2, based on expert knowledge that cross-device alarm effects in large telecommunication networks generally do not exceed two geodesic distances. Besides, the impact of this parameter on the final result is minimal. -Regularization Coefficients \lambda1 and \lambda2: We estimate their magnitude through gradient methods by comparing the loss difference between two training steps with the difference in the regularization terms. -Pruning Threshold \rho: After training, we print the entire weight matrix. Generally, there is an order of magnitude gap between the weights of edges and non-edges, allowing us to determine the appropriate threshold.
2.Impact of Expert Knowledge: In our specific application to telecommunication networks, the expert knowledge we employed, such as the sparsity and acyclicity of the causal graph, has been carefully considered and validated, making it solid within our setting. However, if the actual graph in a different application is not sparse, the assumptions and constraints used in S2GCSL would need to be adjusted to suit the specific characteristics of that problem. This would involve tailoring the assumptions to better reflect the nature of the underlying causal relationships, thereby ensuring the constraints remain relevant and accurate.
3.Observation in Table 1: This is an interesting observation. Our analysis suggests that the efficiency drop in ADM4 for large-scale problems is due to its high memory requirement for processing large matrices. In large networks, it is necessary to reduce the batch size to avoid exceeding memory limits, which compromises efficiency.
Thank you again for your high evaluation and thoughtful feedback. We will incorporate your suggestions to improve the clarity, rigor, and applicability of our paper. Your insights have been invaluable in enhancing our work.
Thank you for the author's response, and I keep my score on this work.
Thanks for your positive feedback and for recognizing the contribution of our work. We are pleased to incorporate all suggestions into the revised version based on your comments. If you have any further questions, please feel free to bring them up and we will address them as soon as possible. Thanks again.
This paper presents S2GCSL, a novel approach for Granger causal structural learning from topological event sequences in telecommunication networks. The methodology leverages a linear kernel to model interactions among event types and employs gradient descent for efficient optimization of the likelihood function. Unlike existing methods, S2GCSL integrates expert knowledge as constraints, enhancing the interpretability of results. Extensive experiments on both synthetic and real-world datasets demonstrate the approach's scalability and efficacy, addressing the challenges of fault diagnosis in large-scale, interconnected network environments.
优点
- The paper presents a highly original approach to telecommunication network fault diagnosis (TNFD) through the development of S2GCSL, a scalable Granger causal structural learning method tailored for topological event sequences.
- The originality is evident in the innovative problem formulation that leverages the unique structure of telecommunication networks, integrating expert knowledge constraints to enhance interpretability and address existing limitations in the field.
- The quality of the research is underscored by a rigorous methodology, employing linear kernels and gradient descent optimization, and validated through extensive experiments on both synthetic and real-world datasets.
- Clarity is maintained throughout the paper, with well-organized sections and clear explanations of complex concepts, making the work accessible to a broad audience.
- The significance of this work is substantial, offering a robust and efficient solution to a critical problem in telecommunication networks, with potential applications extending to other domains such as power grids and transport networks. By addressing scalability and interpretability issues, the paper makes a notable contribution to the fields of causal inference and network fault diagnosis.
缺点
- The method is primarily tested on telecommunication networks. It would be helpful for the authors to discuss the scalability and applicability of the S2GCSL method to other types of network topologies, such as hierarchical, mesh, or dynamically evolving networks. Providing theoretical insights or preliminary experimental results on these different network structures would strengthen the argument for the method’s generalizability.
- It would be better for the paper to discuss how the S2GCSL method can be adapted for online cases where data arrives continuously and decisions need to be made online. This limitation raises concerns about the method's applicability in scenarios requiring immediate fault diagnosis and response in telecommunication networks.
- The paper specifies the computational resources used for the experiments; however, it does not address how the S2GCSL method would perform on less powerful hardware, since in practical deployment, there may be a need for each signal source to perform fault diagnosis online independently, and the computational resources of these signal sources are often limited. This raises concerns about the feasibility of deploying the method in resource-constrained environments, which may limit its practical applicability.
问题
- The method is tested on a specific telecommunication network topology. How well does the S2GCSL method scale to different network topologies, such as hierarchical or mesh networks? Have there been any tests or validations on networks with varying levels of connectivity and complexity?
- The paper uses a linear kernel to model activation interactions among event types. Have other types of kernels been considered or tested, such as polynomial or exponential kernels? What led to the decision to use a linear kernel specifically?
- The synthetic data is generated using specific parameters and distributions. Have other synthetic data generation techniques been considered?
局限性
N/A
Dear Reviewer,
Thank you for your thorough and insightful review of our paper. We appreciate your positive evaluation of our work and your constructive feedback. We address your comments and questions as follows: Weaknesses:
- Applicability to Other Network Topologies: We appreciate the reviewer's insightful comment. The topological network considered in our study is currently the most common setting in the field, consistent with the related works such as TMHP and TNPAR. In our specific problem domain, we have not observed cases of hierarchical or mesh networks. Nonetheless, we acknowledge the value of your suggestion and recognize that further exploration into these network structures could enhance the generalizability of our method.
2.Adaptation for Online Cases: While our experiments were conducted using offline data, it is important to note that event sequences inherently possess temporal characteristics. Therefore, our method can be adapted to online scenarios to a certain extent, making it possible to integrate and apply our approach in real-time environments.
3.Performance on Less Powerful Hardware: A significant advantage of S2GCSL over existing methods is its efficiency. Consequently, in scenarios with limited computational resources, our method naturally offers better performance. We are confident that the efficient nature of S2GCSL makes it more suitable for deployment in resource-constrained environments compared to other approaches.
Questions: 1.Scalability to Different Network Topologies: Our experiments follow the latest and most common setups in this field, focusing on telecommunication network topologies as described in the paper. The hierarchical and mesh networks you mentioned are indeed significant research directions, and we will explore these in future work. Besides, our experiments involve randomly generated adjacency matrices for the topological network, which inherently varies the connectivity density and complexity of the topological network.
2.Choice of Linear Kernel: Our method can also perform well with polynomial, and exponential kernels. However, we chose the linear kernel for its superior simplicity and efficiency.
3.Synthetic Data Generation Techniques: The synthetic data generation method we used is the most popular one in the current topological Granger causal discovery field. Notably, seminal works in the field, such as TNPAR and TMHP, also use this method to generate synthetic data.
We appreciate the opportunity to improve our paper based on your feedback. The suggested additions and clarifications will enhance the comprehensiveness and applicability of our work. Thank you for your time and valuable suggestions.
Thank you for the author's response. My issue has been resolved, and I will maintain my score.
Thanks for your positive feedback and for recognizing the contribution of our work. We are pleased to incorporate all suggestions into the revised version based on your comments. If you have any further questions, please feel free to bring them up and we will address them as soon as possible. Thanks again.
This paper proposes S2GCSL for learning Granger causal structures from topological event sequences, particularly in the context of telecommunication network fault diagnosis (TNFD). The idea is considered novel and innovative (Dc6x, B6jL, 6ZcE), scalable and efficient (Dc6x, B6jL, 6ZcE), and significant for real-world network fault diagnosis (Dc6x, B6jL, wGGW). However, there are concerns about the method's applicability to different network topologies (Dc6x), its robustness to violations of underlying assumptions (B6jL), and its adaptability to online or real-time scenarios (wGGW). Fortunately, the authors have addressed the main issues in the authors' responses. The decision is acceptance.