Abstract
Identification of lonely students is important because loneliness may lead to sickness, depression, and even suicide for college students. Loneliness scales are the general instruments used to identify loners, but it usually fails when loners try to conceal their real conditions in the questionnaires. In this paper, we propose a framework for the identification of loners based on their project collaboration records, a relatively more objective data source than student’s self-reports. Considering that collaborative relationships among students are highly informative for the identification of loners, we employ Graph Neural Networks to model the complex patterns of student interactions. Furthermore, we propose a Graph-based Over-sampling Technique (GOT) to address the class-imbalanced problem for graph-structured data. Experiments on a real-world dataset show that our proposed method can identify loners with high accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Becheru, A., Popescu, E.: Using social network analysis to investigate students’ collaboration patterns in eMUSE platform (2017). https://doi.org/10.1109/ICSTCC.2017.8107045
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1), 107–117 (1998). https://doi.org/10.1016/S0169-7552(98)00110-X. Proceedings of the Seventh International World Wide Web Conference
Chang, E., et al.: Loneliness under assault: Understanding the impact of sexual assault on the relation between loneliness and suicidal risk in college students. Pers. Individ. Differ. 72, 155–159 (2015). https://doi.org/10.1016/j.paid.2014.09.001
Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. (JAIR) 16, 321–357 (2002). https://doi.org/10.1613/jair.953
Crespo, P., Antunes, C.: Predicting teamwork results from social network analysis. Expert Syst. 32, 312–325 (2013). https://doi.org/10.1111/exsy.12038
Douzas, G., Bação, F., Last, F.: Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Inf. Sci. 465, 1–20 (2018). https://doi.org/10.1016/j.ins.2018.06.056
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. arXiv e-prints arXiv:1706.02216 (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Khalid, N.H., Ibrahim, R., Selamat, A., Abdul Kadir, M.R.: Collaboration patterns of researchers using social network analysis approach. pp. 001632–001637 (2016). https://doi.org/10.1109/SMC.2016.7844473
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR abs/1609.02907 (2016). http://arxiv.org/abs/1609.02907
Li, Q., Han, Z., Wu, X.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI-18 AAAI Conference on Artificial Intelligence, pp. 3538–3545 (2018)
Russell, D., Peplau, L., Ferguson, M.: Developing a measure of loneliness. J. Pers. Assess. 42, 290–294 (1978). https://doi.org/10.1207/s15327752jpa4203_11
Sanchez, W., Martínez-Rebollar, A., Campos, W., Estrada Esquivel, H., Pelechano, V.: Inferring loneliness levels in older adults from smartphones. J. Amb. Intell. Smart Environ. 7, 85–98 (2015). https://doi.org/10.3233/AIS-140297
Skues, J., Williams, B., Oldmeadow, J., Wise, L.: The effects of boredom, loneliness, and distress tolerance on problem internet use among university students. Int. J. Ment. Health Addict. 14(2), 167–180 (2015). https://doi.org/10.1007/s11469-015-9568-8
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lió, P., Bengio, Y.: Graph attention networks (2017)
Wood, G.: The structure and vulnerability of a drug trafficking collaboration network. Soc. Netw. 48, 1–9 (2017). https://doi.org/10.1016/j.socnet.2016.07.001
Zhou, J., et al.: Graph neural networks: a review of methods and applications. arXiv Learning (2018). http://arxiv.org/abs/1812.08434
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, Q., Li, J., Tang, Y., Ge, L. (2020). Identifying Loners from Their Project Collaboration Records - A Graph-Based Approach. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12274. Springer, Cham. https://doi.org/10.1007/978-3-030-55130-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-55130-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55129-2
Online ISBN: 978-3-030-55130-8
eBook Packages: Computer ScienceComputer Science (R0)