Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250700014-10.doi: 10.11896/jsjkx.250700014

• Big Data & Data Science • Previous Articles     Next Articles

SINDy-GSN:Sparse Identification of Network Dynamics for Group Behavior in Social Graphs

WANG Yuhan1, MA Fuyuan2, MA Shixuan3, WANG Ying4   

  1. 1 College of Software,Jilin University,Changchun 130012,China
    2 College of Artificial Intelligence,Jilin University,Changchun 130012,China
    3 College of Computer Science and Technology,Jilin University,Changchun 130012,China
    4 Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education,Changchun 130012,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:WANG Yuhan,born in 2001,postgra-duate.Her main research interests include machine learning and deep lear-ning.
    WANG Ying,born in 1981,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.18369S).Her main research interests include machine learning,social networks,data mining and search engines.
  • Supported by:
    Foundation of the Major Project of Scicnce and Technology Innovation 2030-New Generation of Artifiial Intelligence(2021ZD0112500) and National Natural Science Foundation of China(62272191,62372211).

PDF (PC)

20

Abstract: The evolution of group behavior in social networks is often marked by nonlinearity,multi-agent coupling,and structural heterogeneity,posing challenges for traditional modeling methods in uncovering the underlying dynamics.To better capture these dynamics,this paper proposes an improved method for dynamic identification based on a structure-coupled function library—SINDy-GSN.The method leverages feature-driven discrete simulation,integrating user behavior states,adjacency structures,and topic information to construct a tripartite state vector,generating a high-dimensional nonlinear function library suited for social networks.The library incorporates first-order neighbor influence,normalized diffusion,and topic propagation coupling,effectively capturing the dynamic interplay between individual behavior and network structure.Using real-world social platform data,a simulation network is created,and discrete evolution models the propagation of group stances,enabling sparse modeling and identification of group behavior dynamics.Results show that SINDy-GSN maintains interpretability and sparsity while accurately identi-fying group propagation mechanisms,offering a versatile framework for modeling and predicting complex social behaviors,with strong adaptability and scalability.

Key words: Social networks, Function library construction, SINDy, Network dynamics, Sparse regression

CLC Number: 

  • TP393
[1] GAO J,BARZEL B,BARABÁSI A L.Universal resilience patterns in complex networks[J].Nature,2016,530(7590):307-312.
[2] HARUSH U,BARZEL B.Dynamic patterns of information flow in complex networks[J].Nature Communications,2017,8(1):2181.
[3] HENS C,HARUSH U,HABER S,et al.Spatiotemporal signalpropagation in complex networks[J].Nature Physics,2019,15(4):403-412.
[4] BARZEL B,BARABÁSI A L.Universality in network dynamics[J].Nature Physics,2013,9(10):673-681.
[5] BRUNTON S L,PROCTOR J L,KUTZ J N.Discovering governing equations from data by sparse identification of nonlinear dynamical systems[J].Proceedings of the National Academy of Sciences,2016,113(15):3932-3937.
[6] MANGAN N M,BRUNTON S L,PROCTOR J L,et al.Inferring biological networks by sparse identification of nonlinear dynamics[J].IEEE Transactions on Molecular,Biological,and Multi-Scale Communications,2017,2(1):52-63.
[7] KAISER E,KUTZ J N,BRUNTON S L.Sparse identification of nonlinear dynamics for modelpredictive control in the low-data limit[J].Proceedings of the Royal Society A,2018,474(2219):20180335.
[8] LIANG J,ZHANG X,WANG K,et al.Discovering dynamicmodels of COVID-19 transmission[J].Transboundary and Emerging Diseases,2022,69(4):e64-e70.
[9] QUADE M,ABEL M,NATHAN KUTZ J,et al.Sparse identification of nonlinear dynamics for rapid model recovery[J].Chaos:An Interdisciplinary Journal of Nonlinear Science,2018,28(6):063116.
[10] JIANG Y,SUN J.Evo-SINDy:Universal Discovery of Partial Differential Equations Using Cooperative Evolutionary Computation[C]//Proceedings of the Genetic and Evolutionary Computation Conference.2025:791-799.
[11] HASTIE T,TIBSHIRANI R,WAINWRIGHT M.Statisticallearning with sparsity[J].Monographs on Statistics and Applied Probability,2015,143(143):8.
[12] ZHENG P,ASKHAM T,BRUNTON S L,et al.A unifiedframework for sparse relaxed regularizedregression:SR3[J].IEEE Access,2018,7:1404-1423.
[13] FASEL U,KUTZ J N,BRUNTON B W,et al.Ensemble-SINDy:Robust sparse model discovery in the low-data,high-noise limit,with active learning and control[J].Proceedings of the Royal Society A,2022,478(2260):20210904.
[14] PAN S,BRUNTON S L,KUTZ J N.Neural Implicit Flow:a mesh-agnostic dimensionality reductionparadigm of spatio-temporal data[J].Journal of Machine Learning Research,2023,24(41):1-60.
[15] SERVIA M Á C,SANDOVAL I O,HELLGARDT K,et al.The Automated Discovery of Kinetic Rate Models--Methodological Frameworks[J].arXiv:2301.11356,2023.
[16] SOROKINA M,SYGLETOS S,TURITSYN S.Sparse identification for nonlinear optical communication systems:SINO me-thod[J].Optics Express,2016,24(26):30433-30443.
[17] DAM M,BRØNS M,JUUL RASMUSSEN J,et al.Sparse identification of a predator-prey system from simulation data of a convection model[J].Physics of Plasmas,2017,24(2):022310.
[18] LI R,SUN C,DONG M,et al.The controllability analysis of brain networks during rhythmic propagation[J].IEEE Transactions on Network Science and Engineering,2024,11(4):3812-3823.
[19] NAKARMI U,RAHNAMAY-NAEINI M,KHAMFROUSHH.Critical component analysis in cascading failures for power grids using community structures in interaction graphs[J].IEEE Transactions on Network Science and Engineering,2019,7(3):1079-1093.
[20] KOLEY S.Critically reckoning spectrophotometric detection of asymptomatic cyanotoxins and faecal contamination in periurban agrarian ecosystems via convolutional neural networks[J].Trends in Sciences,2024,21(12):8528-8528.
[21] ZHANG Q,YU K,GUOZ,et al.Graph neural network-driven traffic forecasting for the connected internet of vehicles[J].IEEE Transactions on Network Science and Engineering,2021,9(5):3015-3027.
[22] DONG Y,ZHAN M,KOU G,et al.A survey on the fusionprocess in opinion dynamics[J].Information Fusion,2018,43:57-65.
[23] DELABAYS R,DE PASQUALE G,DÖRFLER F,et al.Hypergraph reconstruction from dynamics[J].Nature Communications,2025,16(1):2691.
[24] ZHANG L,SCHAEFFER H.On the convergence of the SINDy algorithm[J].Multiscale Modeling & Simulation,2019,17(3):948-972.
[25] BONGARD J,LIPSON H.Automated reverse engineering ofnonlinear dynamical systems[J].Proceedings of the National Academy of Sciences,2007,104(24):9943-9948.
[26] SCHMIDT M,LIPSON H.Distilling free-form natural lawsfrom experimental data[J].Science,2009,324(5923):81-85.
[27] KOZA J R.Genetic programming:On the programming of computers by means of natural selection[M].MIT Press,1992.
[28] GEAR C W,HYMAN J M,KEVREKIDID P G,et al.Equation-free,coarse-grained multiscale computation:Enabling mocroscopic simulators to perform system-level analysis[J].Commiunications in Mathematical Sciences,2003,1(4):715-762.
[29] RAISSI M,PERDIKARIS P,KARNIADAKIS G E.Physics-informed neural networks:A deep learning framework for solving forward and inverse problems involving nonlinear partial diffe-rential equations[J].Journal of Computational Physics,2019,378:686-707.
[30] LONG Z,LU Y,DONG B.PDE-Net 2.0:Learning PDEs from data with a numeric-symbolic hybrid deep network[J].Journal of Computational Physics,2019,399:108925.
[31] STEPHANY R,EARLS C.PDE-READ:Human-readable partial differential equation discovery using deep learning[J].Neural Networks,2022,154:360-382.
[32] LU L,JIN P,PANG G,et al.Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators[J].Nature machine intelligence,2021,3(3):218-229.
[33] CAMPS-VALLS G,GERHARDUS A,NINAD U,et al.Disco-vering causal relations and equations from data[J].Physics Reports,2023,1044:1-68.
[34] XU H,CHANG H,ZHANG D.DLGA-PDE:Discovery of PDEs with incomplete candidate library via combination of deep lear-ning and genetic algorithm[J].Journal of Computational Physics,2020,418:109584.
[35] CHEN Y,LUO Y,LIU Q,et al.Symbolic genetic algorithm for discovering open-form partial differential equations(SGA-PDE)[J].Physical Review Research,2022,4(2):023174.
[36] MASLYAEV M,HVATOV A,KALYUZHNAYA A V.Partial differential equations discovery with EPDE framework:Application for real and synthetic data[J].Journal of Computational Science,2021,53:101345.
[37] ATKINSON S,SUBBER W,WANG L,et al.Data-driven discovery of free-form governing differential equations[J].arXiv:1910.05117,2019.
[38] IVANCHIK E,HVATOV A.Knowledge-aware differentialequation discovery with automated background knowledge extraction[J].Information Sciences,2025,712:122131.
[39] MASLYAEV M,HVATOV A.Equation discovery framework EPDE:Towards a better equation discovery[J].arXiv:2501.14768,2025.
[40] WANG Y,LIU Y,LIU N,et al.AdaGCL+:An Adaptive Subgraph Contrastive Learning Towards Tackling Topological Bias[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2025,47(9):8073-8087.
[41] HE X,WANG Y,FAN W,et al.Mamba-based graph convolu-tional networks:Tackling over-smoothing with selective state space[J].arXiv:2501.15461,2025.
[42] JUAN X,PENG M,WANG X.Dynamic self-training with less uncertainty for graph imbalance learning[J].Expert Systems with Applications,2025,271:126643.
[1] GUO Xuan, HOU Jinlin, WANG Wenjun, JIAO Pengfei. Dynamic Link Prediction Method for Adaptively Modeling Network Dynamics [J]. Computer Science, 2025, 52(6): 118-128.
[2] CHEN Zhangyuan, CHEN Ling, LIU Wei, LI Bin. Method for Selecting Observers Based on Doubly Resolving Set in Independent Cascade Model [J]. Computer Science, 2025, 52(4): 280-290.
[3] LIU Wei, WU Fei, GUO Zhen, CHEN Ling. Two Stage Rumor Blocking Method Based on EHEM in Social Networks [J]. Computer Science, 2024, 51(7): 156-166.
[4] DENG Ziwei, CHEN Ling, LIU Wei. Continuous Influence Maximization Under Independent Cascade Propagation Model [J]. Computer Science, 2024, 51(6): 161-171.
[5] ZHU Yangfu, LI Meiling, TAN Jiachen, WU Bin. Study on Text-based Personality Detection－A Review [J]. Computer Science, 2024, 51(12): 209-222.
[6] CAO Jinxin, XU Weizhong, JIN Di, DING Weiping. Survey of Community Discovery in Complex Networks [J]. Computer Science, 2023, 50(11A): 230100130-11.
[7] WEI Peng, MA Yu-liang, YUAN Ye, WU An-biao. Study on Temporal Influence Maximization Driven by User Behavior [J]. Computer Science, 2022, 49(6): 119-126.
[8] GUO Lei, MA Ting-huai. Friend Closeness Based User Matching [J]. Computer Science, 2022, 49(3): 113-120.
[9] SHAO Yu, CHEN Ling, LIU Wei. Maximum Likelihood-based Method for Locating Source of Negative Influence Spreading Under Independent Cascade Model [J]. Computer Science, 2022, 49(2): 204-215.
[10] ZHANG Shao-jie, LU Xu-dong, GUO Wei, WANG Shi-peng, HE Wei. Prevention of Dishonest Behavior in Supply-Demand Matching [J]. Computer Science, 2021, 48(4): 303-308.
[11] YUAN De-yu, CHEN Shi-cong, GAO Jian, WANG Xiao-juan. Intervention Algorithm for Distorted Information in Online Social Networks Based on Stackelberg Game [J]. Computer Science, 2021, 48(3): 313-319.
[12] MA Li-bo, QIN Xiao-lin. Topic-Location-Category Aware Point-of-interest Recommendation [J]. Computer Science, 2020, 47(9): 81-87.
[13] LIANG Jun-bin, ZHANG Min, JIANG Chan. Research Progress of Social Sensor Cloud Security [J]. Computer Science, 2020, 47(6): 276-283.
[14] WU Lei,YUE Feng,WANG Han-ru,WANG Gang. Academic Paper Recommendation Method Combined with Researcher Tag [J]. Computer Science, 2020, 47(2): 51-57.
[15] GU Qiu-yang, JU Chun-hua, WU Gong-xing. Social Network Information Recommendation Model Combining Deep Autoencoder and Network Representation Learning [J]. Computer Science, 2020, 47(11): 101-112.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.