Abstract
Team formation is to find a group of experts covering required skills and well collaborating together. Existing studies suffer from two defects: cannot afford flexible designation of team members and do not consider whether the formed team is truly adopted in practice. In this paper, we propose the Predictive Team Formation (PTF) problem. PTF provides the flexibility of designated members and delivers the prediction-based formulation to compose the team. We propose two methods by learning the feature representations of experts based on node2vec [4]. One is Biased-n2v that models the topic bias of each expert in the social network. The other is Guided-n2v that refines the transition probabilities between skills and experts to guide the random walk in a heterogeneous graph of expert-expert, expert-skill, and skill-skill. Experiments conducted on DBLP and IMDb datasets exhibit that our methods can significantly outperform the state-of-the-art optimization-based approaches in terms of prediction recall. We also reveal that the designated members with tight social connections can lead to better performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., Leonardi, S.: Online team formation in social networks. In: Proceedings of ACM WWW (2012)
Anzai, Y.: Pattern Recognition and Machine Learning. Elsevier, New York (2012)
Basiri, J., Taghiyareh, F., Ghorbani, A.: Collaborative team formation using brain drain optimization: a practical and effective solution. World Wide Web J. (2017)
Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of ACM SIGKDD (2016)
Kargar, M., Zihayat, M., An, A.: Discovering top-k teams of experts with/without a leader in social networks. In: Proceedings of ACM CIKM (2011)
Kargar, M., Zihayat, M., An, A.: Finding affordable and collaborative teams from a network of experts. In: Proceedings of SDM (2013)
Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of ACM SIGKDD (2009)
Li, C.T., Huang, M.Y., Yan, R.: Team formation with influence maximization for influential event organization on social networks. World Wide Web J. (2017)
Li, C.T., Shan, M.K.: Composing activity groups in social networks. In: Proceedings of ACM CIKM (2012)
Li, C.T., Shan, M.K., Lin, S.D.: On team formation with expertise query in collaborative social networks. KAIS (2015)
Li, K., Lu, W., Bhagat, S., Lakshmanan, L.V., Yu, C.: On social event organization. In: Proceedings of the 20th ACM SIGKDD (2014)
Li, L., Tong, H., Cao, N., Ehrlich, K., Lin, Y.R., Buchler, N.: Replacing the irreplaceable: fast algorithms for team member recommendation. In: Proceedings of ACM WWW (2015)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of Workshop at ICLR (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS (2013)
Rangapuram, S.S., Bühler, T., Hein, M.: Towards realistic team formation in social networks based on densest subgraphs. In: Proceedings of ACM WWW (2013)
Sozio, M., Gionis, A.: The community-search problem and how to plan a successful cocktail party. In: Proceedings of ACM SIGKDD (2010)
Teng, Y.C., Wang, J.Z., Huang, J.L.: Team formation with the communication load constraint in social networks. In: Peng, W.C. et al. (eds.) PAKDD 2014. LNCS, vol. 8643, pp. 125–136. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-319-13186-3_12
Tong, Y., She, J., Meng, R.: Bottleneck-aware arrangement over event-based social networks: the max-min approach. World Wide Web J. (2016)
Yang, D.N., Shen, C.Y., Lee, W.C., Chen, M.S.: On socio-spatial group query for location-based social networks. In: Proceedings of ACM SIGKDD (2012)
Yu, Z., Zhang, D., Yu, Z., Yang, D.: Participant selection for offline event marketing leveraging location-based social networks. IEEE Trans. Syst. Man Cybern.: Syst. (2015)
Acknowledgment
This paper was supported in part by Ministry of Science and Technology, R.O.C., under Contract 105-2221-E-006-140-MY2, 106-3114-E-006-002, 107-2636-E-006-002, and 106-2628-E-006-005-MY3, and by Academia Sinica under AS-107-TP-A05.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Ting, L.PY., Li, CT., Chuang, KT. (2018). Predictive Team Formation Analysis via Feature Representation Learning on Social Networks. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_62
Download citation
DOI: https://doi.org/10.1007/978-3-319-93040-4_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93039-8
Online ISBN: 978-3-319-93040-4
eBook Packages: Computer ScienceComputer Science (R0)