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A Latent Parameter Node-Centric Model for Spatial Networks

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  • Nicholas D Larusso
  • Brian E Ruttenberg
  • Ambuj Singh

Abstract

Spatial networks, in which nodes and edges are embedded in space, play a vital role in the study of complex systems. For example, many social networks attach geo-location information to each user, allowing the study of not only topological interactions between users, but spatial interactions as well. The defining property of spatial networks is that edge distances are associated with a cost, which may subtly influence the topology of the network. However, the cost function over distance is rarely known, thus developing a model of connections in spatial networks is a difficult task. In this paper, we introduce a novel model for capturing the interaction between spatial effects and network structure. Our approach represents a unique combination of ideas from latent variable statistical models and spatial network modeling. In contrast to previous work, we view the ability to form long/short-distance connections to be dependent on the individual nodes involved. For example, a node's specific surroundings (e.g. network structure and node density) may make it more likely to form a long distance link than other nodes with the same degree. To capture this information, we attach a latent variable to each node which represents a node's spatial reach. These variables are inferred from the network structure using a Markov Chain Monte Carlo algorithm. We experimentally evaluate our proposed model on 4 different types of real-world spatial networks (e.g. transportation, biological, infrastructure, and social). We apply our model to the task of link prediction and achieve up to a 35% improvement over previous approaches in terms of the area under the ROC curve. Additionally, we show that our model is particularly helpful for predicting links between nodes with low degrees. In these cases, we see much larger improvements over previous models.

Suggested Citation

  • Nicholas D Larusso & Brian E Ruttenberg & Ambuj Singh, 2013. "A Latent Parameter Node-Centric Model for Spatial Networks," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-15, September.
  • Handle: RePEc:plo:pone00:0071293
    DOI: 10.1371/journal.pone.0071293
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    References listed on IDEAS

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    1. Fragkiskos Papadopoulos & Maksim Kitsak & M. Ángeles Serrano & Marián Boguñá & Dmitri Krioukov, 2012. "Popularity versus similarity in growing networks," Nature, Nature, vol. 489(7417), pages 537-540, September.
    2. Wong, Ling Heng & Pattison, Philippa & Robins, Garry, 2006. "A spatial model for social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(1), pages 99-120.
    3. Peter D. Hoff, 2009. "Multiplicative latent factor models for description and prediction of social networks," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 261-272, December.
    4. Deniz Turgut & Ali Rana Atilgan & Canan Atilgan, 2010. "Assortative Mixing in Close-Packed Spatial Networks," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-9, December.
    5. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    6. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    7. Marián Boguñá & Fragkiskos Papadopoulos & Dmitri Krioukov, 2010. "Sustaining the Internet with hyperbolic mapping," Nature Communications, Nature, vol. 1(1), pages 1-8, December.
    8. Peter D. Hoff, 2005. "Bilinear Mixed-Effects Models for Dyadic Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 286-295, March.
    9. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
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