Abstract
In this paper, we address the problem of research role discovery, especially for large research institutes where similar yet separated teams co-exist. The roles that researchers play in a research team, i.e., principal investigator, sub-investigator and research staff, typically exhibit an ordinal relationship. In order to better incorporate the ordinal relationship into a role discovery model, we approach research role discovery as an ordinal regression problem. In the proposed approach, we represent a research team as a heterogeneous teamwork network and propose OrdinalFM, short for Ordinal Factorization Machines, to learn the role prediction function. OrdinalFM extends the traditional Factorization Machines (FM) in an effort to handle the ordinal relationship among learning targets. Experiments with a real-world research team dataset verify the advantages of OrdinalFM over state-of-the-art ordinal regression methods.
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Beckham, C., Pal, C.: Unimodal probability distributions for deep ordinal classification, pp. 411–419 (2017)
Brandes, U., Lerner, J.: Structural similarity: spectral methods for relaxed blockmodeling. J. Classif. 27(3), 279–306 (2010)
Cheng, Y., Agrawal, A., et al.: Social role identification via dual uncertainty minimization regularization. In: Proceedings of the IEEE ICDM, pp. 767–772 (2014)
Chu, W., Keerthi, S.S.: New approaches to support vector ordinal regression. In: Proceedings of the 22nd ICML, pp. 145–152 (2005)
Gilpin, S., Eliassi-Rad, T., Davidson, I.: Guided learning for role discovery (GLRD). In: Proceedings of the 19th ACM SIGKDD, pp. 113–121 (2013)
Gu, B., Sheng, V.S., Tay, K.Y., Romano, W., Li, S.: Incremental support vector learning for ordinal regression. IEEE TNNLS 26(7), 1403–1416 (2015)
Gutiérrez, P.A., Tiňo, P., Hervás-Martínez, C.: Ordinal regression neural networks based on concentric hyperspheres. Neural Netw. 59, 51–60 (2014)
Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O.: Centrality indices. In: Brandes, U., Erlebach, T. (eds.) Network Analysis. LNCS, vol. 3418, pp. 16–61. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31955-9_3
Lin, H.-T., Li, L.: Large-margin thresholded ensembles for ordinal regression: theory and practice. In: Balcázar, J.L., Long, P.M., Stephan, F. (eds.) ALT 2006. LNCS, vol. 4264, pp. 319–333. Springer, Heidelberg (2006). https://doi.org/10.1007/11894841_26
Liu, X., Zou, Y., Song, Y., Yang, C., You, J., Kumar, B.V.K.V.: Ordinal regression with neuron stick-breaking for medical diagnosis. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11134, pp. 335–344. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11024-6_23
Nowicki, K., Snijders, T.A.B.: Estimation and prediction for stochastic blockstructures. J. Am. Stat. Assoc. 96(455), 1077–1087 (2001)
Pei, Y., Zhang, J., Fletcher, G.H.: DyNMF: role analytics in dynamic social networks. In: Proceedings of the 27th IJCAI, pp. 3818–3824 (2018)
Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. (TIST) 3(3), 57 (2012)
Xiao, Y., Liu, B., Hao, Z.: Multiple-instance ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. (2018)
Zhao, Y., Wang, G., Yu, P.S., Liu, S., Zhang, S.: Inferring social roles and statuses in social networks. In: Proceedings of the 19th ACM SIGKDD, pp. 695–703 (2013)
Acknowledgement
This work is partially supported by Natural Science Foundation of China (61602278, 71704096 and 31671588) and SDUST Higher Education Research Project (2015ZD03).
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Liu, T., Ni, W., Zeng, Q., Xie, N. (2019). Predictive Role Discovery of Research Teams Using Ordinal Factorization Machines. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_13
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DOI: https://doi.org/10.1007/978-3-030-26072-9_13
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