Vladimir Ouzienko, Prediction of Attributes and Links in Temporal Social Networks

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Citation

Vladimir Ouzienko, Yuhong Guo and Zoran Obradovic. Prediction of Attributes and Links in Temporal Social Networks. Proceeding of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence.

Online Version

Prediction of Attributes and Links in Temporal Social Networks

Datasets

Experiments were conducted on two synthetic datasets and two real life datasets - Delinquency and Teenagers. The Delinquency consists of 4 temporal observation of 26 students where for each observation, the researchers collected delinquency measure (5 points scale score). The Teenagers consists of 3 temporal observations of 50 students, where for each observation the measurement of the students alcohol consumption was taken (also 5 points scale score).

Summary

This paper comes up a new model named Temporal Exponential Random Graphical Model(tERGM) to predict the attributes and links in Temporal Social Network. Mathematical theory and formula are presented in this paper to show how this model works in predicting nodes and links in a temporal social network. Additionally, some experiments based on this model are conducted to show the performance of this model. According to the paper's experiments results, this model has a fairly accurate prediction and lower MSE in prediction.

Experiments

This paper conducted some experiments by apply the tERGM to 4 different datasets, 2 of them are sythetic, the other two are real-life ones. The accuracy of the model and also the MSE between the prediction and the truth value.

Table experiment.jpg

In both experiments on synthetic and real life data the etERGM clearly outperformed the conventional predictors in prediction of actor’s attributes while the difference in predicting links for Teenagers was inconclusive which could be expected for a network of low density where the prediction problem is very difficult.

Self Comments

This model cannot be used in large-scale network according to my investigation. The author also admitted in the email, they only tested the model in a 500 nodes network. And it takes almost 50 hours to run the code. So, the model is still not perfect and may not be practical when applied to large-scale network. Further improvement is needed if planed to use in a large-scale network.

Related Papers

  1. O. Frank and D. Strauss, ‘Markov graphs’, Journal of the American Statistical Association, 81, 832–842, (1986).
  2. F. Guo, S. Hanneke, W. Fu, and E. P. Xing, ‘Recovering temporally rewiring networks: a model-based approach’, in ICML ’07: Proceedings of the 24th international conference on Machine learning, pp. 321–328, New York, NY, USA, (2007). ACM.
  3. S. Hanneke and E. Xing, ‘Discrete temporal models of social networks’, in Proceedings of the ICML 06 Workshop on Statistical Network Analysis. Springer-Verlag (2006).
  4. L. Michell and A. Amos, ‘Girls, pecking order and smoking’, Social Science and Medicine, 44(12), 1861 – 1869, (1997).
  5. T. Snijders, C. Steglich, and G. van de Bunt, ‘Introduction to stochastic actor-based models for network dynamics’, Social Networks, (2009).
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