Comprehensive Analysis of Single and Multi-Purpose Function-Based Community Detection over Social Media
How to Cite?
Rishank Rathore, Ravi Kumar Singh Pippal, "Comprehensive Analysis of Single and Multi-Purpose Function-Based Community Detection over Social Media," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 108-117, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I2P212
Abstract
In parallel with the development of the Internet, social networks have become attractive as research topics in many different disciplines, and the most accurate systems are expressed in complex networks. The most common feature of complex networks is their community structure, where the connections within the node groups are more closely related to the rest of the network. Identifying major clusters and community structures allows discovering organizational rules of complex networks such as web charts and biological networks. In general, the communities seem to overlap. Overlap is when an individual belongs to more than one social group and is one of the characteristic features of social networks. In recent years, overlapping community discovery has received much attention in the application areas of social networks. Many methods using different tools and techniques have been proposed to solve the overlapping community discovery problem. This paper gives a comparative analysis over heuristic overlap community detection algorithm over social media and presents a comprehensive analysis of single and multi-purpose functions for community detection.
Keywords
Community detection, Heuristic Classification, Optimization Algorithm, Single Function, Multi-Purpose Function, Seed Community.
Reference
[1] Social networks using hierarchical community detection, in 7th Conference on Information and Knowledge Technology (IKT), (2015) 1–5.
[2] Karimi, S.Lotfi, and H. Izadkhah, Communityguided link prediction in multiplex networks, Journal of Informetrics, 15(4) (2021) 101178.
[3] A. Fang, The influence of communication structure on opinion dynamics in social networks with multiple true states, Applied Mathematics and Computation, 406 (2021) 126262.
[4] W. Liu, T. Suzumura, L. Chen, and G. Hu, A generalized incremental bottom-up community detection framework for highly dynamic graphs, in 2017 IEEE International Conference on Big Data (Big Data), (2017) 3342–3351.
[5] Q. Chen and L. Wei, Overlapping community detection of complex network, A survey, in 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), (2019) 513–516, 2019.
[6] D. K. Singh, R. A. Haraty, N. C. Debnath, and P. Choudhury, An analysis of the dynamic community detection algorithms in complex networks, in 2020 IEEE International Conference on Industrial Technology (ICIT), (2020) 989–994.
[7] S. Liu and Z. Li, A modified genetic algorithm for community detection in complex networks, in 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), (2017) 1–3.
[8] B. Shen, N. Wang, and H. Qiu, A new genetic algorithm for overlapping community detection, in 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, (2014) 766–769.
[9] M. Guerrero, F. G. Montoya, R. BaAos, A. Alcayde, and C. Gil, Adaptive community detection in complex networks using genetic algorithms, Neurocomputing, 266 (2017) 101–113.
[10] K. Fatania, D. Joshi, T. Patalia, and Y. Jejani, A comparison of overlapping community detection in a large complex network, in International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC), (2019) 1–6.
[11] C. He, H. Liu, Y. Tang, S. Liu, X. Fei, Q. Cheng, andH. Li, Similarity preserving overlapping community detection in signed networks, Future Generation Computer Systems, 116 (2021) 275–290.
[12] Y. Yan and H. Xie, Detection of hierarchical overlapping communities in complex networks, in International Conference on Intelligent Computing, Automation and Systems (ICICAS), (2019) 893–896.
[13] D. DEMIROL, F. OZTEMIZ, and A. KARCI, Performance comparison of physics-based meta-heuristic optimization algorithms, in International Conference on Artificial Intelligence and Data Processing (IDAP), (2018) 1–5.
[14] G. Rossetti, D. Pedreschi, and F. Giannotti, Nodecentric community discovery, From static to dynamic social network analysis, Online Social Networks and Media, 3(4) (2017) 32–48.
[15] I. Litou and V. Kalogeraki, Pythia, A system for online topic discovery of social media posts, in 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), (2017) 2497–2500.
[16] V. Moscato and G. SperlA, A survey about community detection over online social and heterogeneous information networks, Knowledge-Based Systems, 224 (2021) 107112
[17] M. Li, L. Wen, and F. Chen, A novel collaborative filtering recommendation approach based on soft coclustering, Physica A, Statistical Mechanics and its Applications, 561 (2021) 125140.
[18] R. Vatrapu, R. R. Mukkamala, A. Hussain, and B. Flesch, Social set analysis, A set-theoretical approach to big data analytics, IEEE Access, 4 (2016) 2542–2571.
[19] S. Acer, E. G. Boman, C. A. Glusa, and S. Rajamanickam, Sphynx, A parallel multigpu graph partitioner for distributed-memory systems, Parallel Computing, (2021) 102769.
[20] F. J. BaldA˜ ¡n and J. M. BenAtez, Multivariate time-series classification through an interpretable representation, Information Sciences, 569 (2021) 596–614.
[21] H. Van Lierde, T. W. S. Chow, and G. Chen, Scalable spectral clustering for overlapping community detection in large-scale networks, IEEE Transactions on Knowledge and Data Engineering, 32(4) (2020) 54–767.
[22] H. Jain and G. Harit, Unsupervised temporal segmentation of human action using community detection, in 2018 25th IEEE International Conference on Image Processing (ICIP), (2018) 1892–1896.
[23] N. F. Haq, M. Moradi, and Z. J. Wang, Community structure detection from networks with weighted modularity, Pattern Recognition Letters, 122 (2019) 14–22.
[24] M. Ebrahimi, M. R. Shahmoradi, Z.Heshmati, andM. Salehi, A novel method for overlapping community detection using multiobjective optimization, Physical A, Statistical Mechanics and its Applications, 505 (2018) 825–835.
[25] R. Dong, J. Yang, and Y. Chen, Overlapping community detection in weighted temporal text networks, IEEE Access, 8 (2020) 58118–58129.
[26] A. Karaaslanli and S. Aviyente, Constrained spectral clustering for dynamic community detection, in ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2020) 8474–8478.
[27] W. Zang, X. Ji, S. Liu, and G. Wang, Percolation on interdependent networks with cliques and weak interdependence, Physica A, Statistical Mechanics and its Applications, 566 (2021) 125612, 2021.
[28] M. A. Javed, M. S. Younis, S. Latif, J. Qadir, and A. Baig, Community detection in networks, A multidisciplinary review, Journal of Network and Computer Applications, 108 (2018) 87–111.
[29] Y. Zhou and F. Xu, Research on application of artificial-intelligence algorithm in a directed graph, in 2017 International Conference on Computing Intelligence and Information System (CIIS), (2017) 116–120.
[30] M. Ehteram, A. Ferdowsi, M. Faramarzpour, A. M. S.Al-Janabi, N. Al-Ansari, N. D. Bokde, and Z. M. Yaseen, Hybridization of artificial intelligence models with nature-inspired optimization algorithms for lake water level prediction and uncertainty analysis, Alexandria Engineering Journal, 60(2) (2021) 2193–2208, 2021.
[31] T. Li, J. Yang, and D. Cui, Artificial-intelligence-based algorithms in multi-access edge computing for the performance optimization control of a benchmark microgrid, Physical Communication, 44 (2021) 101240.
[32] E. Ficarella, L. Lamberti, and S. Degertekin, Comparison of three novel hybrid metaheuristic algorithms for structural optimization problems, Computers and Structures, 244 (2021) 106395.
[33] M. Dehghani, M. Mashayekhi, and M. Sharifi, An efficient imperialist competitive algorithm with likelihood assimilation for topology, shape and sizing optimization of truss structures, Applied Mathematical Modelling, 93 (2021) 1–27.
[34] Y. Ma, X.Zhang, J.Song, and L. Chen, A modified teaching learning-based optimization algorithm for solving an optimization problem, Knowledge-Based Systems, 212 (2021) 106599.
[35] Y. Zhang and Z. Jin, Group teaching optimization algorithm, A novel metaheuristic method for solving global optimization problems, Expert Systems with Applications, 148 (2020) 113246.
[36] A. Ammar and S. ul Islam Ahmad, Teaching-learning based optimization algorithm for core reload pattern optimization of a research reactor, Annals of Nuclear Energy, 133 (2019) 169–177.
[37] A. K. Shukla, P. Singh, and M. Vardhan, An adaptive inertia weight teaching-learning-based optimization algorithm and its applications, Applied Mathematical Modelling, 77 (2020) 309–326.
[38] M. Kumar, A. J. Kulkarni, and S. C. Satapathy, Social evolution learning optimization algorithm, A socioinspired optimization methodology, Future Generation Computer Systems, 81 (2018) 252–272.
[39] Y. Zhang and P. Zhang, Machine training and parameter settings with a social-emotional optimization algorithm for support vector machine, Pattern Recognition Letters, 54 (2015) 36–42.
[40] P. S. Pal, S. Choudhury, A. Ghosh, S. Kumar, R. Kar, D. Mandal, and S. P. Ghoshal, Social-emotional optimization algorithm-based identification of nonlinear Hammerstein model, in 2016 International Conference on Communication and Signal Processing (ICCSP), (2016) 1633– 1637.
[41] A. Rauniyar, R. Nath, and P. K. Muhuri, Modified brain storm optimization algorithm in objective space for a pollution-routing problem, in 2019 IEEE Congress on Evolutionary Computation (CEC), (2019) 242–247.
[42] Z. Dai, W. Fang, K. Tang, and Q. Li, An optimaidentified framework with brain storm optimization for multimodal optimization problems, Swarm and Evolutionary Computation, 62 (2021) 100827.
[43] L. Jain, R. Katarya, and S. Sachdeva, Opinion leader detection using whale optimization algorithm in online social network, Expert Systems with Applications, 142 (2020) 113016.
[44] X. Feng, Y. Wang, H. Yu, and F. Luo, A novel intelligence algorithm based on the social group optimization behaviours, IEEE Transactions on Systems, Man, and Cybernetics, Systems, 48(1) (2018) 65–76.
[45] A. Ishizaka, B. Lokman, and M. Tasiou, A stochastic multi-criteria divisive hierarchical clustering algorithm, Omega, vol. 103, p. 102370, 2021.
[46] J. Sheng, J. Hu, Z. Sun, B. Wang, A. Ullah, K. Wang, and J. Zhang, Community detection based on human social behaviour, Physica A, Statistical Mechanics and its Applications, 531 (2019) 121765.
[47] J. Ma, J. Zhang, Y. Lin, and Z. Dai, Cost-efficiency trade-offs of the human brain network revealed by a multiobjective evolutionary algorithm, NeuroImage, 236 (2021) 118040.
[48] A. Aylani and N. Goyal, Community detection in social network-based non user as social activities, in 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), (2017) 625–628.
[49] W. W. Zachary, An information flow model for conflict and fission in small groups, Journal of Anthropological Research, 33(4) (1977) 452–473.
[50] D. Lusseau, The emergent properties of a dolphin social network, Proceedings of the Royal Society of London. Series B, Biological Sciences, 270(2) (2003) S186– S188.
[51] M. Girvan and M. E. J. Newman, Community structure in social and biological networks, Proceedings of the National Academy of Sciences, 99 (2002) 7821–7826.
[52] M. E. J. Newman, Modularity and community structure in networks, Proceedings of the National Academy of Sciences, 103(23) (2006) 8577–8582.