MODELING Modeling user success in online social networks using advanced GNN architectures
DOI:
https://doi.org/10.61591/jslhu.20.624Keywords:
Online social network; Professional success; Graph convolutional neural networks; Social graph.Abstract
Online social networks (OSNs) provide extensive data reflecting users’ personalities, interests, and social connections. The study explores how graph convolutional neural networks (GCNNs) can be used to analyze data from the VKontakte social network to predict users' professional success. Using features like user profiles and social connections, it evaluates various GCNN architectures, including GCNConv, SAGEConv, and GINConv. The Graph Isomorphism Network (GIN) layer achieved the highest accuracy (0.88). This research highlights the effectiveness of advanced neural networks in understanding professional success metrics in online social networks. Hong Bang International University, Ho Chi Minh City, Vietnam
References
Buettner R. Predicting user behavior in electronic markets based on personality-mining in large online social networks. Electron. Mark 2017, 27(3), pp. 247–265.
Martono G. H., et al. An extended approach of weight collective influence graph for detection influence actor Int. J. Adv. Intell. Informatics 2022, 8(1), pp. 1–11, 2022.
Ulizulfa A., et al. Temperament detection based on Twitter data: classical machine learning versus deep learning. Int. J. Adv. Intell. Informatics, 8(1), pp. 45–57, 2022.
Alzubaidi L. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 2021, 8(1), pp. 1–74.
Qiu J., et al. DeepInf: Social influence prediction with deep learning. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 2110–2119, 2018.
Gao L., et al., HetInf: Social Influence Prediction with Heterogeneous Graph Neural Network. Front. Phys., 9, p. 729, 2022.
Shang L., et al. FauxWard: a graph neural network approach to fauxtography detection using social media comments. Soc. Netw. Anal. Min., 10(1), pp. 1–16, 2020.
P. Zhu et al. SI-News: Integrating social information for news recommendation with attention-based graph convolutional network. Neurocomputing, 494, pp. 33–42, 2022.
C. Zhang, et al. Inferring Users’ Social Roles with a Multi-Level Graph Neural Network Model. Entropy 2021, 23(11), p. 1453, 2021.
Y. Xie, et al. A Multi-Task Representation Learning Architecture for Enhanced Graph Classification. Front. Neurosci., 13, pp. 1395, 2020.
Mueller T. T. et al. Differentially Private Graph Classification with GNNs 2022.
T. Le, et al., “Parameterized Hypercomplex Graph Neural Networks for Graph Classification,” Lect. Notes Comput. Sci, 12893, pp. 204–216, 2021.
D. Quercia, et al., “The personality of popular facebook users,” Proc. ACM Conf. Comput. Support. Coop. Work. CSCW, pp. 955–964, 2012.
M. Saqr, U. Fors, and J. Nouri, “Using social network analysis to understand online Problem-Based Learning and predict performance,” PLoS One, 13(9), p. e0203590, 2018.
M. Saqr and A. Alamro, “The role of social network analysis as a learning analytics tool in online problem based learning,” BMC Med. Educ., 19(1), pp. 1–11, 2019.
A. M. Bhandarkar, A. K. Pandey, R. Nayak, K. Pujary, and A. Kumar, “Impact of social media on the academic performance of undergraduate medical students,” Med. J. Armed Forces India, 77, pp. S37–S41, 2021.
K. Toteva and E. Gourova, “Social network analysis in professional e-recruitment,” Adv. Intell. Soft Comput., 101, pp. 75–80, 2011.
S. Chala and M. Fathi, “Job seeker to vacancy matching using social network analysis,” Proc. IEEE Int. Conf. Ind. Technol., pp. 1250–1255, 2017.
F. M. Gafarov, et al., “A Complex Neural Network Model for Predicting a Personal Success based on their Activity in Social Networks,” Eurasia J. Math. Sci. Technol. Educ., 17(10), p. em2010, 2021.
E. Kay, J. A. Bondy, and U. S. R. Murty, “Graph Theory with Applications,” Oper. Res. Q., 28(1), p. 237, 1977.
F. Scarselli, et al., “The graph neural network model,” IEEE Trans. Neural Networks, 20(1), pp. 61–80, 2009.
J. Zhou et al., “Graph neural networks: A review of methods and applications,” AI Open, 1, pp. 57–81, 2020.
Y. Zhou, et al., “Graph Neural Networks: Taxonomy, Advances, and Trends,” ACM Trans. Intell. Syst. Technol., 13(1), pp. 1, 2022.
T. N. Kipf and M. Welling, “Semi-Supervised Classification with Graph Convolutional Networks,” 5th Int. Conf. Learn.
Represent. ICLR 2017 - Conf. Track Proc., 2016.
N. K. Ahmed et al., “Inductive Representation Learning in Large Attributed Graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035, 2017.
C. Morris et al., “Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks,” Proc. AAAI Conf. Artif. Intell., 33(1), pp. 4602–4609, 2019.
P. Veličković, A. Casanova, P. Liò, G. Cucurull, A. Romero, and Y. Bengio, “Graph Attention Networks,” 6th Int. Conf. Learn. Represent. ICLR 2018 - Conf. Track Proc., 2017.
Y. Shi, Z. Huang, S. Feng, H. Zhong, W. Wang, and Y. Sun, “Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification,” IJCAI Int. Jt. Conf. Artif. Intell., 2, pp. 1548–1554, 2021.
D. Bacciu, et al., “A gentle introduction to deep learning for graphs,” Neural Networks, 129, pp. 203–221, 2020.
F. M. Gafarov, et al., “A Complex Neural Network Model for Predicting a Personal Success based on their Activity in Social Networks,” Eurasia J. Math. Sci. Technol. Educ., 17(10), p. em2010, 2021.
P. E. Pope, et al., “Explainability methods for graph convolutional neural networks,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2019, pp. 10764–10773, 2019.
H. Yuan, et al. Explainability in Graph Neural Networks: A Taxonomic Survey. IEEE Trans. Pattern Anal. Mach. Intell 2020.
