Trade-offs between Agility and Reliability of Predictions in Dynamic Social Networks Used to Model Risk of Microbial Contamination of Food (2009)

Artur Dubrawski, Purnamrita Sarkar, Lujie Chen

Tags

Dynamic Social Networks, Food Safety

Abstract

This paper evaluates trade-offs between agility and reliability of predictions arising due to sparseness of data modeled with dynamic social networks. We use real field data from food safety domain to illustrate the discussion. We model food production facilities as one type of entities in a social evolving in time. Another type of entities denotes various specific strains of Salmonella. Two entities are linked in the graph if a microbial test of food sample conducted at the specific food facility over specific period of time turns out positive for the particular pathogen. We use a computationally efficient latent space model to predict future occurrences of pathogens in individual facilities. Empirical results indicate predictive utility of the proposed representation. However, sparseness of data limits the attainable agility of predictions. We identify exploiting recency of data and using the known patterns in it, such as seasonality, as plausible means of battling the challenge of sparseness.

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Approximate BibTeX Entry

@inproceedings{DubrawskiSarkarChen_ASONAM2009,
    Month = {July},
    Year = {2009},
    Publisher = {IEEE Computer Society},
    Booktitle = {2009 International Conference on Advances in Social Network Analysis and Mining ASONAM 2009},
    Editor = {Reda Alhajj, Nasrullah Memon},
    Author = { Artur Dubrawski, Purnamrita Sarkar, Lujie Chen },
    Title = {Trade-offs between Agility and Reliability of Predictions in Dynamic Social Networks Used to Model Risk of Microbial Contamination of Food}
}