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arxiv: 2605.21220 · v1 · pith:ZXEXJCG6new · submitted 2026-05-20 · 💻 cs.SI

ASIND: Alternating Sparse Identification for Predicting Network Dynamics Without Knowledge

Mingyu Kang , Jianxi Gao , Wenwu Yu , Linyuan Lv This is my paper

Pith reviewed 2026-05-21 01:11 UTC · model grok-4.3

classification 💻 cs.SI
keywords network dynamicssparse identificationalternating optimizationsystem identificationsocial network analysistrajectory predictionweak identifiability
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The pith

Alternating identification recovers network dynamics without knowledge

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes an Alternating Sparse Identification of Network Dynamics (ASIND) algorithm that iteratively identifies the self-dynamics function, the interactive function, and the interactive network without any prior knowledge. This addresses the limitations of previous methods that either require known functional forms or rely on non-interpretable universal approximations with vast search spaces. Experiments demonstrate that ASIND achieves state-of-the-art performance in identifying the components and in making accurate predictions for up to 100 steps ahead. Additionally, the results highlight the weak identifiability of the interactive network, meaning multiple different networks can produce very similar dynamics.

Core claim

By alternately performing sparse identification on the self-dynamics function, the interactive function, and the interactive network, the ASIND algorithm can learn interpretable models of network dynamics directly from observed state trajectories without requiring knowledge of any of these components.

What carries the argument

The ASIND algorithm, which alternates between sparse regression steps for self-dynamics, interaction functions, and network structure to minimize prediction error on observed trajectories.

If this is right

  • Accurate long-term prediction of network states becomes possible without prior system knowledge.
  • Interpretable functional forms for dynamics and interactions can be recovered from data.
  • Network structure inference is possible but may not be unique due to weak identifiability.
  • Performance surpasses methods that require known functions or use black-box approximations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Network identification from dynamics data may have inherent ambiguities that limit unique recovery in social network applications.
  • The alternating approach could be tested on real-world datasets from biology or physics to check generalization beyond social systems.
  • Combining the method with additional regularization techniques might improve performance on larger or noisier networks.

Load-bearing premise

That the alternating optimization can successfully disentangle the effects of self-dynamics, interactions, and network structure from the observed time series data.

What would settle it

If applying ASIND to synthetic data generated from known but complex dynamics and network yields recovered components that produce large prediction errors on held-out future steps.

read the original abstract

Identifying network dynamics is a critical yet challenging task to to understand the mechanism of real-world social systems. There are two types of algorithms, and one requires the knowledge of self-dynamics function, interactive function, and interactive network to sparsely identify the network dynamics. Another one does not require any knowledge, but use simple functions to universally approximate complex functions. However, this type of algorithms lack interpretability, and the functional space is too extensive to search efficiently. Thus, to address this issue, this work proposes an Alternating Sparse Identification of Network Dynamics (ASIND) algorithm to sparsely identify the self-dynamics function, interactive function and interactive network alternatively. Extensive experiments are conducted to show the state-of-the-art identification and 100-steps prediction performance compared to the baseline. The experimental results also show the weak identifiability of interactive network, that means different networks can generate highly similar trajectories of network dynamics. The code is available at https://github.com/KMY-SEU/ASIND.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript proposes the Alternating Sparse Identification of Network Dynamics (ASIND) algorithm to identify network dynamics without prior knowledge. It alternately sparsely identifies the self-dynamics function, interactive function, and interactive network from observed trajectories. The abstract reports that extensive experiments demonstrate state-of-the-art identification accuracy and 100-step prediction performance relative to baselines, while also noting the weak identifiability of the interactive network (different networks can produce highly similar trajectories). Code is stated to be available.

Significance. If the performance claims are substantiated, ASIND would offer a useful middle ground between knowledge-dependent sparse methods and non-interpretable universal approximators for modeling social network dynamics. The explicit observation of weak identifiability is a constructive contribution that could inform future identifiability analyses. Releasing code supports reproducibility.

major comments (1)
  1. Abstract: The central claim of 'state-of-the-art identification and 100-steps prediction performance' is presented without any information on datasets, baseline methods, error metrics, number of trials, or how post-hoc modeling choices were made. This information is load-bearing for verifying the empirical contribution and cannot be assessed from the provided text.
minor comments (2)
  1. Abstract: Typo 'task to to understand' should read 'task to understand'.
  2. Abstract: The distinction between the two existing algorithm types could be stated more precisely to clarify how ASIND avoids both the knowledge requirement and the overly broad search space.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the single major comment below and will incorporate the suggested changes in the revised version.

read point-by-point responses

  1. Referee: [—] Abstract: The central claim of 'state-of-the-art identification and 100-steps prediction performance' is presented without any information on datasets, baseline methods, error metrics, number of trials, or how post-hoc modeling choices were made. This information is load-bearing for verifying the empirical contribution and cannot be assessed from the provided text.

    Authors: We agree that the abstract would be strengthened by including additional context for the performance claims. In the revised manuscript we will expand the abstract to briefly note the datasets (synthetic networks with known self- and interaction dynamics plus real-world social network trajectories), the baseline methods (knowledge-dependent sparse identification approaches and universal approximators), the error metrics (identification error and multi-step prediction error), the number of independent trials, and the criteria used for post-hoc model selection. This will make the empirical contribution more readily verifiable while preserving the abstract's brevity. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The available text consists solely of the abstract, which describes an algorithmic procedure (ASIND) for alternately identifying self-dynamics, interactive functions, and the network from trajectories. No equations, derivations, or mathematical claims are presented that could be inspected for reduction to inputs by construction, self-definition, or fitted predictions. The proposal is framed as addressing limitations of prior algorithm types without invoking self-citations or uniqueness theorems in the given text. This is a standard non-finding for summaries lacking detailed technical content, consistent with the expectation that most papers exhibit no circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated assumption that the alternating procedure converges to useful sparse representations from trajectory data alone.

pith-pipeline@v0.9.0 · 5676 in / 1030 out tokens · 25773 ms · 2026-05-21T01:11:41.243131+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages

  1. [1]

    Science China Information Sciences , volume=

    Systems science in the new era: intelligent systems and big data , author=. Science China Information Sciences , volume=

    work page

  2. [2]

    IEEE Transactions on Systems, Man, and Cybernetics, Part B , year=

    Yu, Wenwu and Chen, Guanrong and Cao, Ming and Kurths, Jürgen , title=. IEEE Transactions on Systems, Man, and Cybernetics, Part B , year=

    work page

  3. [3]

    Science China Information Sciences , year =

    Mingyu Kang and Duxin Chen and Ziyuan Pu and Jianxi Gao and Wenwu Yu , title =. Science China Information Sciences , year =

    work page

  4. [4]

    IEEE Transactions on Artificial Intelligence , year=

    Kang, Mingyu and Chen, Duxin and Meng, Ning and Yan, Gang and Yu, Wenwu , title=. IEEE Transactions on Artificial Intelligence , year=

    work page

  5. [5]

    IEEE Transactions on Network Science and Engineering , year=

    He, Yanyan and Kang, Mingyu and Chen, Duxin and Yu, Wenwu , title=. IEEE Transactions on Network Science and Engineering , year=

    work page

  6. [6]

    Proceedings of the National Academy of Sciences , volume=

    Discovering governing equations from data by sparse identification of nonlinear dynamical systems , author=. Proceedings of the National Academy of Sciences , volume=. 2016 , publisher=

    work page 2016

  7. [7]

    Science Advances , volume=

    Data-driven discovery of partial differential equations , author=. Science Advances , volume=. 2017 , publisher=

    work page 2017

  8. [8]

    Nature Communications , volume=

    Data driven discovery of cyber physical systems , author=. Nature Communications , volume=. 2019 , publisher=

    work page 2019

  9. [9]

    Advances in Neural Information Processing Systems , volume=

    Neural ordinary differential equations , author=. Advances in Neural Information Processing Systems , volume=

    work page

  10. [10]

    Nature Machine Intelligence , volume=

    Closed-form continuous-time neural networks , author=. Nature Machine Intelligence , volume=. 2022 , publisher=

    work page 2022

  11. [11]

    Chaos: An Interdisciplinary Journal of Nonlinear Science , volume=

    Structural inference of networked dynamical systems with universal differential equations , author=. Chaos: An Interdisciplinary Journal of Nonlinear Science , volume=. 2023 , publisher=

    work page 2023

  12. [12]

    Nature Computational Science , volume=

    Autonomous inference of complex network dynamics from incomplete and noisy data , author=. Nature Computational Science , volume=. 2022 , publisher=

    work page 2022

  13. [13]

    Proceedings of the National Academy of Sciences , volume=

    Predicting network dynamics without requiring the knowledge of the interaction graph , author=. Proceedings of the National Academy of Sciences , volume=. 2022 , publisher=

    work page 2022

  14. [14]

    Physical Review Letters , volume=

    Reconstructing Network Dynamics of Coupled Discrete Chaotic Units from Data , author=. Physical Review Letters , volume=. 2023 , publisher=

    work page 2023

  15. [15]

    Nature Physics , volume=

    Universality in network dynamics , author=. Nature Physics , volume=. 2013 , publisher=

    work page 2013

  16. [16]

    and Renyi, A

    Erdos, P. and Renyi, A. , title =. Publicationes Mathematicae Debrecen , volume =

    work page

  17. [17]

    Nature , volume=

    Collective dynamics of `small-world' networks , author=. Nature , volume=

    work page

  18. [18]

    Emergence of scaling in random networks , journal =

    Barab. Emergence of scaling in random networks , journal =

    work page

  19. [19]

    Reviews of Modern Physics , volume=

    The Kuramoto model: A simple paradigm for synchronization phenomena , author=. Reviews of Modern Physics , volume=. 2005 , publisher=

    work page 2005

  20. [20]

    2016 , author =

    The Kuramoto model in complex networks , journal =. 2016 , author =

    work page 2016

  21. [21]

    1975 , publisher =

    The Mathematical Theory of Infectious Diseases and Its Applications (2nd edition) , author =. 1975 , publisher =

    work page 1975

  22. [22]

    Physical Review Letters , volume=

    Epidemic spreading in scale-free networks , author=. Physical Review Letters , volume=. 2001 , publisher=

    work page 2001

  23. [23]

    1970 , author =

    Species packing and competitive equilibrium for many species , journal =. 1970 , author =

    work page 1970

  24. [24]

    Nature Communications , volume =

    Harush, Uzi and Barzel, Baruch , title =. Nature Communications , volume =. 2017 , issn =

    work page 2017

  25. [25]

    2019 , publisher=

    An introduction to systems biology: design principles of biological circuits , author=. 2019 , publisher=

    work page 2019

  26. [26]

    Nature , volume=

    Universal resilience patterns in complex networks , author=. Nature , volume=. 2016 , publisher=

    work page 2016