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Reconstructing the evolution history of networked complex systems

Published 22 Mar 2024 in physics.soc-ph and cs.SI | (2403.14983v1)

Abstract: The evolution processes of complex systems carry key information in the systems' functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.

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References (55)
  1. Complex networks: Structure and dynamics. Physics Reports, 424(4-5):175–308, 2006.
  2. Mark E. J. Newman. The structure and function of complex networks. SIAM Review, 45(2):167–256, 2003.
  3. Dynamical processes on complex networks. Cambridge University Press, 2008.
  4. High-quality binary protein interaction map of the yeast interactome network. Science, 322(5898):104–110, 2008.
  5. String v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Research, 43(D1):D447–D452, 2015.
  6. Estimating the size of the human interactome. Proceedings of the National Academy of Sciences, 105(19):6959–6964, 2008.
  7. Network link prediction by global silencing of indirect correlations. Nature Biotechnology, 31(8):720–725, 2013.
  8. Ecological networks and their fragility. Nature, 442(7100):259–264, 2006.
  9. Jordi Bascompte. Structure and dynamics of ecological networks. Science, 329(5993):765–766, 2010.
  10. Identification of influential spreaders in complex networks. Nature Physics, 6(11):888–893, 2010.
  11. Analysis of topological characteristics of huge online social networking services. In Proceedings of the 16th International Conference on World Wide Web, WWW ’07, page 835–844, New York, NY, USA, 2007.
  12. Mark E. J. Newman. Assortative mixing in networks. Physical Review Letters, 89(20):208701, 2002.
  13. Collective dynamics of ‘small-world’ networks. Nature, 393(6684):440–442, 1998.
  14. Network motifs: Simple building blocks of complex networks. Science, 298(5594):824–827, 2002.
  15. Emergence of scaling in random networks. Science, 286(5439):509–512, 1999.
  16. Mathematical results on scale-free random graphs. In Handbook of Graphs and Networks, chapter 1, pages 1–34. John Wiley & Sons, Ltd, 2002.
  17. Santo Fortunato. Community detection in graphs. Physics Reports, 486(3):75–174, 2010.
  18. A survey of transfer learning. Journal of Big Data, 3:9, 2016.
  19. Node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, page 855–864, New York, NY, USA, 2016.
  20. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pages 701–710, New York, NY, USA, 2014.
  21. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15, page 1067–1077, Republic and Canton of Geneva, CHE, 2015.
  22. Struc2vec: Learning node representations from structural identity. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, page 385–394, New York, NY, USA, 2017.
  23. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, page 1225–1234, New York, NY, USA, 2016.
  24. Gerald Tesauro. Connectionist learning of expert preferences by comparison training. In Proceedings of the 1st International Conference on Neural Information Processing Systems, NIPS’88, page 99–106, Cambridge, MA, USA, 1988.
  25. Peter Emerson. The original borda count and partial voting. Social Choice and Welfare, 40(2):353–358, 2013.
  26. The evolutionary dynamics of protein-protein interaction networks inferred from the reconstruction of ancient networks. Plos One, 8(3):1–15, 2013.
  27. Regulatory evolution across the protein interaction network. Nature Genetics, 36(10):1059–1060, 2004.
  28. Evolution of the yeast protein interaction network. Proceedings of the National Academy of Sciences, 100(22):12820–12824, 2003.
  29. Scaling up real networks by geometric branching growth. Proceedings of the National Academy of Sciences, 118(21):e2018994118, 2021.
  30. The hidden hyperbolic geometry of international trade: World trade atlas 1870–2013. Scientific Reports, 6(1):1–10, 2016.
  31. Conditions for viral influence spreading through multiplex correlated social networks. Physical Review X, 4(2):021031, 2014.
  32. Detecting and modelling real percolation and phase transitions of information on social media. Nature Human Behaviour, 5(9):1161–1168, 2021.
  33. Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science, 340(6136):1090–1093, 2013.
  34. Cooperative investment in public goods is kin directed in communal nests of social birds. Ecology Letters, 17(9):1141–1148, 2014.
  35. The multilayer temporal network of public transport in Great Britain. Scientific Data, 2(1):1–8, 2015.
  36. Maurice G. Kendall. A new measure of rank correlation. Biometrika, 30(1/2):81–93, 1938.
  37. Charles Spearman. The proof and measurement of association between two things. The American Journal of Psychology, 15(1):72–101, 1961.
  38. Popularity versus similarity in growing networks. Nature, 489(7417):537–540, 2012.
  39. Competition and multiscaling in evolving networks. Europhysics Letters, 54(4):436–442, 2001.
  40. Network mapping by replaying hyperbolic growth. IEEE/ACM Transactions on Networking, 23(1):198–211, 2015.
  41. Network biology: understanding the cell’s functional organization. Nature Reviews Genetics, 5(2):101–113, 2004.
  42. Prediction of functional sites by analysis of sequence and structure conservation. Protein Science, 13(4):884–892, 2004.
  43. Andreas Wagner. The Yeast Protein Interaction Network Evolves Rapidly and Contains Few Redundant Duplicate Genes. Molecular Biology and Evolution, 18(7):1283–1292, 2001.
  44. Mark E. J. Newman. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582, 2006.
  45. Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3):590–614, 2002.
  46. Measuring preferential attachment in evolving networks. Europhysics Letters, 61(4):567–572, 2003.
  47. Drug-target network. Nature Biotechnology, 25(10):1119–1126, 2007.
  48. Andrew L. Hopkins. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology, 4(11):682–690, 2008.
  49. Network medicine framework for identifying drug-repurposing opportunities for covid-19. Proceedings of the National Academy of Sciences, 118(19):e2025581118, 2021.
  50. E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1-2):115–153, 2001.
  51. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 19(3):355–369, 2007.
  52. Temporal link prediction using matrix and tensor factorizations. ACM Transactions on Knowledge Discovery from Data, 5(2):1–27, 2011.
  53. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7):1019–1031, 2007.
  54. Friends and neighbors on the web. Social Networks, 25(3):211–230, 2003.
  55. Reconstructing the evolution history of networked complex systems. evolution_restore. 10.5281/zenodo.10722738, 2024.
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