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Improved artificial bee colony algorithm based on community detection for link prediction problem

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Abstract

The problem of link prediction has recently gained a lot of attention from various domains, including sociology, anthropology, information science, and computer science. In this research, we formulated the link prediction problem as an optimization problem to predict the links of any type of network and proposed two artificial bee colony-based link prediction algorithms enhanced with some neighborhood structures borrowed from genetic algorithms. In an attempt to complete the network’s missing links, the first algorithm optimizes weights, which are used in a linear combination with local and global similarity indexes, to capture the best of both schemes and compensate for each approach’s weaknesses. In the second one, we make use of the community structure of the network to help predict the missing links present in the networks. Experimental results of the proposed algorithms on a number of real-world and synthetic networks provided interesting insights about the problem and showed that the proposed algorithms can enhance link prediction accuracy and are quite competitive with state-of-the-art algorithms.

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Data Availability

The datasets analyzed during the current study are available in the Konect repository, http://konect.cc/networks/. And also in the Network Repository, https://networkrepository.com/index.php

References

  1. Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Networks 25:211–230. https://doi.org/10.1016/S0378-8733(03)00009-1

    Article  Google Scholar 

  2. Aziz F, Gul H, Muhammad I, Uddin I (2020) Link prediction using node information on local paths. Physica A Stat Mech Appl 557:124980. https://doi.org/10.1016/j.physa.2020.124980

    Article  Google Scholar 

  3. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512. https://doi.org/10.1016/S0378-4371(00)00018-2

    Article  MathSciNet  Google Scholar 

  4. Barham R, Aljarah I, (2017) Link prediction based on whale optimization algorithm. In, (2017) International conference on new trends in computing sciences (ICTCS), pp 55–60. IEEE. https://doi.org/10.1109/ICTCS.2017.41

  5. Barham R, Sharieh A, Sleit A (2018) Moth flame optimization based on golden section search and its application for link prediction problem. Mod Appl Sci 13:10–27. https://doi.org/10.5539/mas.v13n1p10

    Article  Google Scholar 

  6. Barham RS, Sharieh A, Sleit A (2019) A meta-heuristic framework based on clustering and preprocessed datasets for solving the link prediction problem. J Inf Sci 45:794–817. https://doi.org/10.1177/0165551518816296

    Article  Google Scholar 

  7. Biswas A, Biswas B (2017) Community-based link prediction. Multimedia Tools Appl 76:18619–18639. https://doi.org/10.1007/s11042-016-4270-9

  8. Bliss CA, Frank MR, Danforth CM, Dodds PS (2014) An evolutionary algorithm approach to link prediction in dynamic social networks. J Comput Sci 5:750–764. https://doi.org/10.1016/j.jocs.2014.01.003

    Article  MathSciNet  Google Scholar 

  9. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008:P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008

    Article  Google Scholar 

  10. Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25:163–177. https://doi.org/10.1080/0022250X.2001.9990249

    Article  Google Scholar 

  11. Chen B, Chen L (2014) A link prediction algorithm based on ant colony optimization. Applied Intelligence 41. https://doi.org/10.1007/s10489-014-0558-5

  12. Daud NN, Ab Hamid SH, Saadoon M, Sahran F, Anuar NB (2020) Applications of link prediction in social networks:A review. J Netw Comput Appl, pp 102716. https://doi.org/10.1016/j.jnca.2020.102716

  13. Del Ser J, Osaba E, Molina D, Yang X-S, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello CAC, Herrera F (2019) Bio-inspired computation: Where we stand and what’s next. Swarm Evol Comput 48:220–250. https://doi.org/10.1016/j.swevo.2019.04.008

    Article  Google Scholar 

  14. Ding J, Jiao L, Wu J, Liu F (2016) Prediction of missing links based on community relevance and ruler inference. Knowl-Based Syst 98:200–215. https://doi.org/10.1016/j.knosys.2016.01.034

    Article  Google Scholar 

  15. Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174. https://doi.org/10.1016/j.physrep.2009.11.002

    Article  MathSciNet  Google Scholar 

  16. Golbeck J (2015) Chapter 21 - analyzing networks. In: Golbeck J (Ed.) Introduction to social media investigation, pp 221–235 Boston: Syngress. https://doi.org/10.1016/B978-0-12-801656-5.00021-4

  17. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey:artificial bee colony (abc) algorithm and applications. Artif Intell Rev 42:21–57. https://doi.org/10.1007/s10462-012-9328-0

    Article  Google Scholar 

  18. Karaboga D et al (2005) An idea based on honey bee swarm for numerical optimization. Technical Report Technical report-tr06, Erciyes university, engineering faculty, computer engineering department

  19. Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18:39–43. https://doi.org/10.1007/BF02289026

    Article  Google Scholar 

  20. Kumar A, Singh SS, Singh K, Biswas B (2020) Link prediction techniques, applications, and performance: A survey. Physica A Stat Mech Appl 553:124289. https://doi.org/10.1016/j.physa.2020.124289

    Article  MathSciNet  Google Scholar 

  21. Kumari A, Behera RK, Sahoo B, Sahoo SP (2022) Prediction of link evolution using community detection in social network. Computing, pp 1–22. https://doi.org/10.1007/s00607-021-01035-4

  22. Kunegis J (2013a) Konect: The koblenz network collection. In: Proceedings of the 22nd international conference on world wide web WWW ’13 Companion, pp 1343–1350. New York, NY, USA Association for computing machinery. https://doi.org/10.1145/2487788.2488173

  23. Kunegis J (2013b) Konect:the koblenz network collection. In: Proceedings of the 22nd international conference on world wide web, pp 1343–1350. https://doi.org/10.1145/2487788.2488173

  24. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Physical review E 78:046110. https://doi.org/10.1103/PhysRevE.78.046110

    Article  Google Scholar 

  25. Lei C, Ruan J (2013) A novel link prediction algorithm for reconstructing protein-protein interaction networks by topological similarity. Bioinformatics 29:355–364. https://doi.org/10.1093/bioinformatics/bts688

    Article  Google Scholar 

  26. Li L, Cheng Y, Tan L, Niu B (2011) A discrete artificial bee colony algorithm for tsp problem. In: International conference on intelligent computing, pp 566–573. Springer. https://doi.org/10.1007/978-3-642-24553-4_75

  27. Li Z, Fang X, Sheng ORL (2017) A survey of link recommendation for social networks: Methods, theoretical foundations, and future research directions. ACM Trans Manag Inf Syst 9:1–26. https://doi.org/10.1145/3131782

    Article  Google Scholar 

  28. Liben-Nowell D, Kleinberg J (2003) The link prediction problem for social networks. In: Proceedings of the twelfth international conference on information and knowledge management, pp 556–559. https://doi.org/10.1145/956863.956972

  29. Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Assoc Inf Sci Technol 58:1019–1031. https://doi.org/10.1002/asi.20591

    Article  Google Scholar 

  30. Lü L, Zhou T (2011) Link prediction in complex networks: A survey. Physica A Stat Mech Appl 390:1150–1170. https://doi.org/10.1016/j.physa.2010.11.027

    Article  Google Scholar 

  31. Luo J-D (2005) Particularistic trust and general trust: A network analysis in chinese organizations. Manag Organ Rev 1:437–458. https://doi.org/10.1111/j.1740-8784.2005.00022.x

    Article  Google Scholar 

  32. Martínez V, Galiano FB, Cubero JC (2016) A survey of link prediction in complex networks. ACM Comput Surv 49:69:1–69:33. https://doi.org/10.1145/3012704

  33. Mastrandrea R, Fournet J, Barrat A (2015) Contact patterns in a high school:a comparison between data collected using wearable sensors, contact diaries and friendship surveys. PloS one 10:e0136497. https://doi.org/10.1371/journal.pone.0136497

    Article  Google Scholar 

  34. Newman MEJ (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74:036104. https://doi.org/10.1103/PhysRevE.74.036104

    Article  MathSciNet  Google Scholar 

  35. Opsahl T, Panzarasa P (2009) Clustering in weighted networks. Soc Networks 31:155–163. https://doi.org/10.1016/j.socnet.2009.02.002

    Article  Google Scholar 

  36. Osaba E, Villar-Rodriguez E, Del Ser J, Nebro AJ, Molina D, LaTorre A, Suganthan PN, Coello CAC, Herrera F (2021) A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm Evol Comput 64:100888. https://doi.org/10.1016/j.swevo.2021.100888

    Article  Google Scholar 

  37. Özcan A, Öğüdücü ŞG (2016) Temporal link prediction using time series of quasi-local node similarity measures. In, (2016) 15th IEEE international conference on machine learning and applications (ICMLA), pp 381–386. IEEE. https://doi.org/10.1109/ICMLA.2016.0068

  38. Pulipati S, Somula R, Parvathala BR (2021) Nature inspired link prediction and community detection algorithms for social networks:a survey. Int J Syst Assur Eng Manag, pp 1–18. https://doi.org/10.1007/s13198-021-01125-8

  39. Raut P, Khandelwal H, Vyas G (2020) A comparative study of classification algorithms for link prediction. In: (2020) 2nd International conference on innovative mechanisms for industry applications (ICIMIA), pp 479–483. IEEE. https://doi.org/10.1109/ICIMIA48430.2020.9074840

  40. Rossi R, Ahmed N (2015) The network data repository with interactive graph analytics and visualization. In: Proceedings of the AAAI conference on artificial intelligence, vol 29. https://doi.org/10.1609/aaai.v29i1.9277

  41. Safdari H, Contisciani M, De Bacco C (2022) Reciprocity, community detection, and link prediction in dynamic networks. J Phys Complex 3:015010. https://doi.org/10.1088/2632-072X/ac52e6

    Article  Google Scholar 

  42. Salha-Galvan G, Lutzeyer JF, Dasoulas G, Hennequin R, Vazirgiannis M (2022) Modularity-aware graph autoencoders for joint community detection and link prediction. Neural Netw 153:474–495. https://doi.org/10.1016/j.neunet.2022.06.021

    Article  Google Scholar 

  43. Shang K-k, Small M (2022) Link prediction for long-circle-like networks. Physical review E 105:024311. https://doi.org/10.1103/PhysRevE.105.024311

  44. Singh SS, Mishra S, Kumar A, Biswas B (2020) Clp-id:Community-based link prediction using information diffusion. Inf Sci 514:402–433. https://doi.org/10.1016/j.ins.2019.11.026

    Article  Google Scholar 

  45. Soundarajan S, Hopcroft J (2012) Using community information to improve the precision of link prediction methods. In: Proceedings of the 21st international conference on World Wide Web, pp 607–608. https://doi.org/10.1145/2187980.2188150

  46. Srilatha P, Manjula R (2017) Structural similarity based link prediction in social networks using firefly algorithm. In: (2017) International conference on smart technologies for smart nation (SmartTechCon), pp 560–564. IEEE. https://doi.org/10.1109/SmartTechCon.2017.8358434

  47. Wang P, Xu B, Wu Y, Zhou X (2014) Link prediction in social networks:the state-of-the-art. Sci China Inf Sci 58:1–38. https://doi.org/10.48550/ar**v.1411.5118

  48. Yan B, Gregory S (2012) Finding missing edges in networks based on their community structure. Phys Rev E 85:056112. https://doi.org/10.1103/PhysRevE.85.056112

    Article  Google Scholar 

  49. Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473. https://doi.org/10.1086/jar.33.4.3629752

    Article  Google Scholar 

  50. Zeng S (2016) Link prediction based on local information considering preferential attachment. Physica A Stat Mech Appl 443:537–542. https://doi.org/10.1016/j.physa.2015.10.016

    Article  Google Scholar 

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Authors and Affiliations

  1. Ecole Militaire Polytechnique, Bordj el Bahri, 16046, Algiers, Algeria

    Hassen Mohamed Kerkache & Lamia Sadeg-Belkacem

  2. Laboratoire des méthodes de conception de systèmes (LMCS), Ecole nationale Supérieure d’Informatique (ESI), BP 68M, Oued-Smar, 16270, Alger, Algeria

    Fatima Benbouzid-Si Tayeb

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Kerkache, H.M., Sadeg-Belkacem, L. & Tayeb, F.BS. Improved artificial bee colony algorithm based on community detection for link prediction problem. Multimed Tools Appl 83, 41655–41681 (2024). https://doi.org/10.1007/s11042-023-17197-6

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