Advanced Analytics of Large Connected Data Based on Similarity Modeling
Collecting various types of data about users/clients in order to improve the services and competitiveness of companies has a long history. However, these approaches are often based on classical statistical methods and an assumption of limited computational power. In this paper we introduce the vision of our applied research project targeting to the financial sector. Our main goal is to develop an automated software solution for similarity modeling over big and semi-structured graph data representing behavior of bank clients. The main aim of similarity models is to improve the decision process in risk management, marketing, security and related areas.
KeywordsSimilarity modeling Big Data Analysis of graph data Transactional data Linked data
This work was supported in part by the Technology Agency of the Czech Republic (TAČR) project no. TH03010276 and by Czech Science Foundation (GAČR) project no. 17-22224S.
- 1.Byeon, W., Breuel, T.M., Raue, F., Liwicki, M.: Scene labeling with LSTM recurrent neural networks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3547–3555, June 2015Google Scholar
- 5.Dong, Y., Chawla, N.V., Swami, A.: Metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, New York, NY, USA, pp. 135–144. ACM (2017)Google Scholar
- 6.Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, New York, NY, USA, pp. 855–864. ACM (2016)Google Scholar
- 7.Hu, Q., Xie, S., Zhang, J., Zhu, Q., Guo, S., Yu, P.S.: Heterosales: utilizing heterogeneous social networks to identify the next enterprise customer. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Republic and Canton of Geneva, Switzerland, pp. 41–50. International World Wide Web Conferences Steering Committee (2016)Google Scholar
- 8.Liu, Q., Xiang, B., Chen, E., Xiong, H., Tang, F., Yu, J.X.: Influence maximization over large-scale social networks: a bounded linear approach. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, New York, NY, USA, pp. 171–180. ACM (2014)Google Scholar
- 10.Zhang, J., Cui, L., Yu, P.S., Lv, Y.: BL-ECD: broad learning based enterprise community detection via hierarchical structure fusion. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, New York, NY, USA, pp. 859–868. ACM (2017)Google Scholar