Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks

Article Sidebar

PDF
Published: Aug 18, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i8s.7191
Keywords:
Online Social Networks (OSNs), community detection, user interest, feature selection, preprocessing

Main Article Content

M. Selvakumar
Research Scholar, Department of Computer Science, Periyar University, Salem – 636011, India
A. Vijaya Kathiravan
Assistant Professor and Head, Department of Computer Applications, Sri Meenakshi Government Arts College for Women, Madurai – 625002, India

Abstract

Online Social Networks (OSNs) have become increasingly popular, with hundreds of millions of users in recent years. A community in a social network is a virtual group with shared interests and activities that they want to communicate. OSN and the growing number of users have also increased the need for communities. Community structure is an important topological property of OSN and plays an essential role in various dynamic processes, including the diffusion of information within the network. All networks have a community format, and one of the most continually addressed research issues is the finding of communities. However, traditional techniques didn't do a better community of discovering user interests. As a result, these methods cannot detect active communities.  To tackle this issues, in this paper presents Densenet Convolution Neural Network (DnetCNN) approach for community detection. Initially, we gather dataset from Kaggle repository. Then preprocessing the dataset to remove inconsistent and missing values. In addition to User Behavior Impact Rate (UBIR) technique to identify the user URL access, key term and page access. After that, Web Crawling Prone Factor Rate (WCPFR) technique is used find the malicious activity random forest and decision method. Furthermore, Spider Web Cluster Community based Feature Selection (SWC2FS) algorithm is used to choose finest attributes in the dataset. Based on the attributes, to find the community group using Densenet Convolution Neural Network (DnetCNN) approach. Thus, the experimental result produce better performance than other methods.

Article Details

How to Cite
Selvakumar, M. ., & Kathiravan, A. V. . (2023). Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks. International Journal on Recent and Innovation Trends in Computing and Communication, 11(8s), 202–214. https://doi.org/10.17762/ijritcc.v11i8s.7191
Issue
Vol. 11 No. 8s (2023)
Section
Articles

References

E. D. Raj, G. Manogaran, G. Srivastava and Y. Wu, "Information Granulation-Based Community Detection for Social Networks," in IEEE Transactions on Computational Social Systems, vol. 8, no. 1, pp. 122-133, Feb. 2021, doi: 10.1109/TCSS.2019.2963247.

R. Aktunc, P. Karagoz and I. H. Toroslu, "Event Detection via Tracking the Change in Community Structure and Communication Trends," in IEEE Access, vol. 10, pp. 109712-109728, 2022, doi: 10.1109/ACCESS.2022.3214543.

K. Chakraborty, S. Bhattacharyya and R. Bag, "A Survey of Sentiment Analysis from Social Media Data," in IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 450-464, April 2020, doi: 10.1109/TCSS.2019.2956957.

W. Luo, B. Duan, L. Ni and Y. Liu, "Collaborative Detection of Community Structure in Multiple Private Networks," in IEEE Transactions on Computational Social Systems, vol. 9, no. 2, pp. 612-623, April 2022, doi: 10.1109/TCSS.2021.3089535.

G. Li and R. M. de Carvalho, "Process Mining in Social Media: Applying Object-Centric Behavioral Constraint Models," in IEEE Access, vol. 7, pp. 84360-84373, 2019, doi: 10.1109/ACCESS.2019.2925105.

A. Sakor, K. Singh and M. -E. Vidal, "Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts," in IEEE Access, vol. 10, pp. 115351-115371, 2022, doi: 10.1109/ACCESS.2022.3217492.

X. Li, G. Xu, W. Lian, H. Xian, L. Jiao and Y. Huang, "Multi-Layer Network Local Community Detection Based on Influence Relation," in IEEE Access, vol. 7, pp. 89051-89062, 2019, doi: 10.1109/ACCESS.2019.2921571.

E. Kafeza, A. Kanavos, C. Makris, G. Pispirigos and P. Vikatos, "T-PCCE: Twitter Personality based Communicative Communities Extraction System for Big Data," in IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 8, pp. 1625-1638, 1 Aug. 2020, doi: 10.1109/TKDE.2019.2906197.

B. A. H. Murshed, J. Abawajy, S. Mallappa, M. A. N. Saif and H. D. E. Al-Ariki, "DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform," in IEEE Access, vol. 10, pp. 25857-25871, 2022, doi: 10.1109/ACCESS.2022.3153675.

C. He, Y. Zheng, X. Fei, H. Li, Z. Hu and Y. Tang, "Boosting Nonnegative Matrix Factorization Based Community Detection With Graph Attention Auto-Encoder," in IEEE Transactions on Big Data, vol. 8, no. 4, pp. 968-981, 1 Aug. 2022, doi: 10.1109/TBDATA.2021.3103213.

Z. Xu et al., "Crowdsourcing Based Description of Urban Emergency Events Using Social Media Big Data," in IEEE Transactions on Cloud Computing, vol. 8, no. 2, pp. 387-397, 1 April-June 2020, doi: 10.1109/TCC.2016.2517638.

S. Zhang, S. Yu, X. E, R. Huo and Z. Sui, "Label Propagation Algorithm Joint Multilayer Neighborhood Overlap and Historic Label Similarity for Community Detection," in IEEE Systems Journal, vol. 16, no. 2, pp. 2626-2634, June 2022, doi: 10.1109/JSYST.2021.3113826.

R. Ren, J. Shao, Y. Cheng and X. Wang, "Detecting Hierarchical and Overlapping Network Communities Based on Opinion Dynamics," in IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 2696-2710, 1 June 2022, doi: 10.1109/TKDE.2020.3014329.

M. Qiao, Y. Wang, S. Wu, A. Luo, S. Ruan and Y. Gu, "Fine-Grained Subjective Partitioning of Urban Space Using Human Interactions From Social Media Data," in IEEE Access, vol. 7, pp. 52085-52094, 2019, doi: 10.1109/ACCESS.2019.2911664.

F. K. Sufi, I. Razzak and I. Khalil, "Tracking Anti-Vax Social Movement Using AI-Based Social Media Monitoring," in IEEE Transactions on Technology and Society, vol. 3, no. 4, pp. 290-299, Dec. 2022, doi: 10.1109/TTS.2022.3192757.

A. Rodriguez, Y. -L. Chen and C. Argueta, "FADOHS: Framework for Detection and Integration of Unstructured Data of Hate Speech on Facebook Using Sentiment and Emotion Analysis," in IEEE Access, vol. 10, pp. 22400-22419, 2022, doi: 10.1109/ACCESS.2022.3151098.

D. Stiawan et al., "An Approach for Optimizing Ensemble Intrusion Detection Systems," in IEEE Access, vol. 9, pp. 6930-6947, 2021, doi: 10.1109/ACCESS.2020.3046246.

W. Han et al., "Improving Training Instance Quality in Aerial Image Object Detection With a Sampling-Balance-Based Multistage Network," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 12, pp. 10575-10589, Dec. 2021, doi: 10.1109/TGRS.2020.3038803.

Joseph Miller, Peter Thomas, Maria Hernandez, Juan González,. Adaptive Decision Making using Reinforcement Learning in Decision Science. Kuwait Journal of Machine Learning, 2(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/204

H. Rong, A. Ramirez-Serrano, L. Guan and Y. Gao, "Image Object Extraction Based on Semantic Detection and Improved K-Means Algorithm," in IEEE Access, vol. 8, pp. 171129-171139, 2020, doi: 10.1109/ACCESS.2020.3025193.

N. Hussain, H. Turab Mirza, I. Hussain, F. Iqbal and I. Memon, "Spam Review Detection Using the Linguistic and Spammer Behavioral Methods," in IEEE Access, vol. 8, pp. 53801-53816, 2020, doi: 10.1109/ACCESS.2020.2979226.

S. Salloum, T. Gaber, S. Vadera and K. Shaalan, "A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques," in IEEE Access, vol. 10, pp. 65703-65727, 2022, doi: 10.1109/ACCESS.2022.3183083.

D. Zhang, J. Han, L. Zhao and T. Zhao, "From Discriminant to Complete: Reinforcement Searching-Agent Learning for Weakly Supervised Object Detection," in IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 12, pp. 5549-5560, Dec. 2020, doi: 10.1109/TNNLS.2020.2969483.

C. Jiménez-Mesa et al., "Optimized One vs One Approach in Multiclass Classification for Early Alzheimer’s Disease and Mild Cognitive Impairment Diagnosis," in IEEE Access, vol. 8, pp. 96981-96993, 2020, doi: 10.1109/ACCESS.2020.2997736.

W. Ai, H. Shao, J. Xu, T. Meng and K. Li, "A Two-Channel Chinese Enterprise Abbreviation Generation Method Based on an Enterprise Component and Single-Character Strategy," in IEEE Access, vol. 10, pp. 81123-81133, 2022, doi: 10.1109/ACCESS.2022.3192965.

S. Nasim, M. S. Almutairi, K. Munir, A. Raza and F. Younas, "A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics," in IEEE Access, vol. 10, pp. 97610-97624, 2022, doi: 10.1109/ACCESS.2022.3205587.