Inference network building and movements prediction based on analysis of induced dependencies

Lin Guo; Wanli Zuo; Tao Peng

Inference network building and movements prediction based on analysis of induced dependencies

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Author(s): Lin Guo ¹ ; Wanli Zuo ² ; Tao Peng ²
- Affiliations: 1: School of Economics and Management, Changchun University of Science and Technology, Jilin 13022, People's Republic of China ;
  2: College of Computer Science and Technology, Jilin University, Jilin 130012, People's Republic of China
Source: Volume 11, Issue 1, February 2017, p. 12 – 17
DOI: 10.1049/iet-sen.2015.0033 , Print ISSN 1751-8806, Online ISSN 1751-8814

Received 10/03/2015, Accepted 12/07/2016, Revised 10/06/2016, Published 01/02/2017

State prediction of a node is made through analyses of adjacent nodes to learn about movements and to manage users in inference network. This study first simplifies ego network to reduce the complexity of structural analyses based on three different relationships among users. Second, degrees of mutual influences are computed according to different forms of association. Finally, the action of the centre node is predicted by analysing the changing of the states of adjacent nodes. The experiment proves that accuracy and efficiency of the algorithm proposed performs well not only in the simplification of ego network but also in the prediction of movements.

References

1. 1)
  - 8. Pearl, J.: ‘Causal inference in statistics: an overview’, Stat. Surv., 2009, 3, pp. 96–146 (doi: 10.1214/09-SS057).
2. 2)
  - 10. Richardson, M., Domingos, P.: ‘Markov logic networks’, Mach. Learn., 2006, 62, (1–2), pp. 107–136 (doi: 10.1007/s10994-006-5833-1).
3. 3)
  - 4. Scripps, J., Tan, P., Esfahanian, A.: ‘Measuring the effects of preprocessing decisions and network forces in dynamic network analysis’. Proc Int. Conf. ACM SIGKDD Int. Conf. on Data Mining (KDD), Paris, France, 2009, pp. 747–756.
4. 4)
  - 12. Andrzejewski, D., Zhu, X., Craven, M., et al: ‘A framework for incorporating general domain knowledge into latent Dirichlet allocation using first-order logic’. Proc Int. Conf. Artificial Intelligence (IJCAI), 2011.
5. 5)
  - 3. Shi, T., Zhu, J.: ‘Online Bayesian passive-aggressive learning’. Proc Int. Conf. 31st Int. Conf. on Machine Learning, 2014, pp. 378–386.
6. 6)
  - 13. Macskassy, S., Provost, F.: ‘A Simple Relational Classifier’. Proc Int. Conf. Workshop on Multi-Relational Data Mining in Conjunction with KDD, Washington, DC, 2003.
7. 7)
  - 11. Pearl, J.: ‘Probabilistic reasoning in intelligent systems: networks of plausible inference’ (Morgan Kaufmann Publishers, San Mateo, 1988), pp. 117–133.
8. 8)
  - 5. Backstrom, L., Kleinberg, J.: ‘Romantic partnerships and the dispersion of social ties: A network analysis of relationship status on facebook’. Proc Int. Conf. 17th ACM Conf. on Computer Supported Cooperative Work and Social Computing (CSCW), 2014.
9. 9)
  - 9. Mei, S., Zhu, J., Zhu, J.: ‘Robust RegBayes: selectively incorporating first-order logic domain knowledge into Bayesian models’. Proc Int. Conf. 31st Int. Conf. on Machine Learning, 2014, pp. 253–261.
10. 10)
  - 7. Anhn, Y., Bagrow, J., Lehmann, S.: ‘Link communities reveal multi-scale complexity in networks’, Nature, 2010, 466, pp. 1476–4687.
11. 11)
  - 1. Tan, C., Tang, J., Sun, J., et al: ‘Social action tracking via noise tolerant time-varying factor graphs’. Proc Int. Conf. ACM SIGKDD Int. Conf. on Data Mining (KDD), Washington, DC, USA, 2010, pp. 1049–1058.
12. 12)
  - 6. Zhou, Y., Liu, L.: ‘Social Influence Based Clustering of Heterogeneous Information Networks’. Proc Int. Conf. 19th ACM SIGKDD Int. Conf. on Knowledge discovery and data mining, 2013.
13. 13)
  - 2. Max, W., Teh, Y.: ‘Bayesian learning via stochastic gradient Langevin dynamics’. Proc Int. Conf. 28th Int. Conf. on Machine Learning, 2011.

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Inference network building and movements prediction based on analysis of induced dependencies

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