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Community detection in network using chronological gorilla troops optimization algorithm with deep learning based weighted convexity

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Abstract

Community is expressed as the subset of nodes in the graph and the interrelation amongst the nodes within the network is deeper than the remaining of the network. Thus, the detection of community in the network is usually designed as a process for representing the network as a tree. Community refining or detection is very crucial in recognizing and evaluating the model of huge as well as complex networks. In this research, the community detection in the network is done based on the split and merge mechanism with the Chronological gorilla troops optimization algorithm (CrGTO) and deep learning-based weighted complexity. Here, the CrGTO is modelled by adapting the chronological concept in the updated location of gorillas to attain better performance. The Gorilla Troops Optimization (GTO) utilizes the present iteration for updating the location of gorillas. Besides, the Chronological concept utilizes the previous iteration for updating the solution. Hence, the GTO algorithm adapts the Chronological concept for updating the optimal location, such that a better solution is achieved. In addition, the devised model utilizes the weighted convexity as a fitness function where the weight in fitness is determined with Deep Belief Network (DBN). Thus, the invented model acquired better performance based on the MI-based betweenness and computational complexity of 0.679 and 0.540.

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Data availability

The data underlying this article are available in Enron email datasets, at http://snap.stanford.edu/data/email-Enron.html. The data underlying this article are available in General Relativity and Quantum Cosmology collaboration (GR-QC) network dataset, at http://snap.stanford.edu/data/ca-GrQc.html. The data underlying this article are available in Condense Matter collaboration (COND-MAT) datasets, at http://snap.stanford.edu/data/ca-CondMat.html.

References

  1. Berahmand, K., Bouyer, A., & Vasighi, M. (2018). Community detection in complex networks by detecting and expanding core nodes through extended local similarity of nodes. IEEE Transactions on Computational Social Systems, 5(4), 1021–1033.

    Article  Google Scholar 

  2. Zhuang, D., Chang, J. M., & Li, M. (2019). DynaMo: Dynamic community detection by incrementally maximizing modularity. IEEE Transactions on Knowledge and Data Engineering, 33(5), 1934–1945.

    Google Scholar 

  3. Javed, M. A., Younis, M. S., Latif, S., Qadir, J., & Baig, A. (2018). Community detection in networks: A multidisciplinary review. Journal of Network and Computer Applications, 108, 87–111.

    Article  Google Scholar 

  4. Rehman, S. U., & Asghar, S. (2020). Online social network trend discovery using frequent subgraph mining. Social Network Analysis and Mining, 10(1), 1–13.

    Article  Google Scholar 

  5. Dineva, K., & Atanasova, T. (2022). Cloud data-driven intelligent monitoring system for interactive smart farming. Sensors, 22(17), 1–26.

    Article  Google Scholar 

  6. Venkataramanan, V., & Lakshmi, S. (2018). A case study of various wireless network simulation tools. International Journal of Communication Networks and Information Security, 10(2), 389–396.

    Google Scholar 

  7. Xuan, Q., Wang, J., Zhao, M., Yuan, J., Fu, C., Ruan, Z., & Chen, G. (2019). Subgraph networks with application to structural feature space expansion. IEEE Transactions on Knowledge and Data Engineering, 33(6), 2776–2789.

    Article  Google Scholar 

  8. Jazaeri, S.S., Taghdiri, E. (2022). Distributed edge computing in SDN-IoT network. Journal of Networking and Communication Systems, 5(2).

  9. Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659, 1–44.

    Article  MathSciNet  Google Scholar 

  10. Berahmand, K., Nasiri, E., Mohammadiani, R.P., Li, Y. (2021). Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding. Computers in Biology and Medicine, 138(1).

  11. Mester, A., Pop, A., Mursa, B. E. M., Greblă, H., Dioşan, L., & Chira, C. (2021). Network analysis based on important node selection and community detection. Mathematics, 9(18), 2294.

    Article  Google Scholar 

  12. Sasidhar, K. (2022). Compressed sensing reconstruction approach using self adaptive butterfly optimization algorithm for bio-signals. Journal of Computational Mechanics, Power System and Control, 5(1).

  13. Aravinth J.S. (2022). Adaptive whale algorithm: Big data classification in IoT networks. Journal of Networking and Communication Systems, 5(2).

  14. Tu, C., Zeng, X., Wang, H., Zhang, Z., Liu, Z., Sun, M., Zhang, B., & Lin, L. (2018). A unified framework for community detection and network representation learning. IEEE Transactions on Knowledge and Data Engineering, 31(6), 1051–1065.

    Article  Google Scholar 

  15. Guo, K., He, L., Chen, Y., Guo, W., & Zheng, J. (2020). A local community detection algorithm based on internal force between nodes. Applied Intelligence, 50(2), 328–340.

    Article  Google Scholar 

  16. Zareie, A., & Sheikhahmadi, A. (2018). A hierarchical approach for influential node ranking in complex social networks. Expert Systems with Applications, 93, 200–211.

    Article  Google Scholar 

  17. Chen, D., Lü, L., Shang, M. S., Zhang, Y. C., & Zhou, T. (2012). Identifying influential nodes in complex networks. Physica a: Statistical mechanics and its applications, 391(4), 1777–1787.

    Article  Google Scholar 

  18. Srinivas, S., & Rajendran, C. (2019). Community detection and influential node identification in complex networks using mathematical programming. Expert Systems with Applications, 135, 296–312.

    Article  Google Scholar 

  19. Ma, T., Liu, Q., Cao, J., Tian, Y., Al-Dhelaan, A., & Al-Rodhaan, M. (2020). LGIEM: Global and local node influence based community detection. Future Generation Computer Systems, 105, 533–546.

    Article  Google Scholar 

  20. Chen, J., Chen, L., Chen, Y., Zhao, M., Yu, S., Xuan, Q., & Yang, X. (2019). GA-based Q-attack on community detection. IEEE Transactions on Computational Social Systems, 6(3), 491–503.

    Article  Google Scholar 

  21. Jazayeri, F., Shahidinejad, A., & Ghobaei-Arani, M. (2021). Autonomous computation offloading and auto-scaling the in the mobile fog computing: A deep reinforcement learning-based approach. Journal of Ambient Intelligence and Humanized Computing, 12, 8265–8284.

    Article  Google Scholar 

  22. Alemayehu, T. S., Kim, J.-H., & Cho, W.-D. (2022). Optimal replacement model for the physical component of safety critical smart-world CPSs. Journal of Ambient Intelligence and Humanized Computing, 13, 4579–4590.

    Article  Google Scholar 

  23. Jayavadivel, R., & Prabaharan, P. (2021). Investigation on automated surveillance monitoring for human identification and recognition using face and iris biometric. Journal of Ambient Intelligence and Humanized Computing, 12, 10197–10208.

    Article  Google Scholar 

  24. Srivastava, A.K., Kumar, S., & Zareapoor, M. (2018). Self-organized design of virtual reality simulator for identification and optimization of healthcare software components. Journal of Ambient Intelligence and Humanized Computing.

  25. Usharani, R., & Shanthini, A. (2021). Neuropathic complications: type II diabetes mellitus and other risky parameters using machine learning algorithms. Journal of Ambient Intelligence and Humanized Computing.

  26. Wang, Y.-C. (2018). Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network. Journal of Ambient Intelligence and Humanized Computing

  27. Di Fazio, A. R., Erseghe, T., Ghiani, E., Murroni, M., Siano, P., & Silvestro, F. (2013). Integration of renewable energy sources, energy storage systems, and electrical vehicles with smart power distribution networks. Journal of Ambient Intelligence and Humanized Computing, 4, 663–671.

    Article  Google Scholar 

  28. Elavarasan, D., & Durai Raj, P. M. (2021). A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters. Journal of Ambient Intelligence and Humanized Computing, 12, 10009–10022.

    Article  Google Scholar 

  29. Dhavakumar, P., & Gopalan, N. P. (2021). An efficient parameter optimization of software reliability growth model by using chaotic grey wolf optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 12(2), 3177–3188.

    Article  Google Scholar 

  30. Abualigah, L., Diabat, A., & Elaziz, M. A. (2023). Improved slime mould algorithm by opposition-based learning and Levy flight distribution for global optimization and advances in real-world engineering problems. Journal of Ambient Intelligence and Humanized Computing, 14, 1163–1202.

    Article  Google Scholar 

  31. Malhat, H. A., Zainud-Deen, A. S., Rihan, M., & Badway, M. M. (2022). Elements failure detection and radiation pattern correction for time-modulated linear antenna arrays using particle swarm optimization. Wireless Personal Communications, 125, 2055–2073.

    Article  Google Scholar 

  32. Grewal, N. S., Rattan, M., & Patterh, M. S. (2017). A non-uniform circular antenna array failure correction using firefly algorithm. Wireless Personal Communications, 97, 845–858.

    Article  Google Scholar 

  33. Šubelj, L. (2018). Convex skeletons of complex networks. Journal of The Royal Society Interface, 15(145), 20180422.

    Article  Google Scholar 

  34. Šubelj, L., Fiala, D., Ciglarič, T., & Kronegger, L. (2019). Convexity in scientific collaboration networks. Journal of Informetrics, 13(1), 10–31.

    Article  Google Scholar 

  35. Mohammadi, M., Moradi, P., & Jalili, M. (2019). SCE: Subspace-based core expansion method for community detection in complex networks. Physica A: Statistical Mechanics and its Applications, 527, 121084.

    Article  Google Scholar 

  36. Ahajjam, S., El Haddad, M., & Badir, H. (2018). A new scalable leader-community detection approach for community detection in social networks. Social Networks, 54, 41–49.

    Article  Google Scholar 

  37. Lu, M., Zhang, Z., Qu, Z., & Kang, Y. (2018). LPANNI: Overlapping community detection using label propagation in large-scale complex networks. IEEE Transactions on Knowledge and Data Engineering, 31(9), 1736–1749.

    Article  Google Scholar 

  38. Beni, H. A., & Bouyer, A. (2020). TI-SC: Top-k influential nodes selection based on community detection and scoring criteria in social networks. Journal of Ambient Intelligence and Humanized Computing, 11(11), 4889–4908.

    Article  Google Scholar 

  39. Xiao, Y., Sun, X., Guo, Y., Li, S., Zhang, Y., & Wang, Y. (2022). An improved gorilla troops optimizer based on lens opposition-based learning and adaptive 13-hill climbing for global optimization. CMES-Computer Modeling in Engineering & Sciences.

  40. Li, C., Wang, Y., Zhang, X., Gao, H., Yang, Y., & Wang, J. (2019). Deep belief network for spectral–spatial classification of hyperspectral remote sensor data. Sensors, 19(1), 204.

    Article  Google Scholar 

  41. Vojt, J. (2016). Deep neural networks and their implementation.

  42. Selvakumar, B., & Muneeswaran, K. (2019). Firefly algorithm based feature selection for network intrusion detection. Computers and Security, 81, 148–155.

    Article  Google Scholar 

  43. Enron email datasets available at “http://snap.stanford.edu/data/email-Enron.html”, accessed on March 2022.

  44. General Relativity and Quantum Cosmology collaboration network datasets available at “http://snap.stanford.edu/data/ca-GrQc.html”, accessed on March 2022.

  45. Condense Matter collaboration datasets available at “http://snap.stanford.edu/data/ca-CondMat.html”, accessed on March 2022.

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Authors and Affiliations

  1. Department of Mathematics, Birla Institute of Technology Mesra, Ranchi, Jharkhand, 835215, India

    Peeyush Tiwari

  2. Department of IT, Ajay Kumar Garg Engineering College, Ghaziabad, Uttar Pradesh, India

    Sundeep Raj

  3. Department of CSE, Birla Institute of Applied Sciences, Bhimtal Nainital, Uttarakhand, 263136, India

    Nitin Chhimwal

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  1. Peeyush Tiwari
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Correspondence to Peeyush Tiwari.

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Tiwari, P., Raj, S. & Chhimwal, N. Community detection in network using chronological gorilla troops optimization algorithm with deep learning based weighted convexity. Wireless Netw 29, 3809–3828 (2023). https://doi.org/10.1007/s11276-023-03430-5

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