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Sussman DL, Tang M, Priebe CE. Consistent latent position estimation and vertex classification for random dot product graphs. IEEE Trans Pattern Anal Mach Intell. 2014;36(1):48-57doi: 10.1109/TPAMI.2013.135.
Sussman, D. L., Tang, M., & Priebe, C. E. (2014). Consistent latent position estimation and vertex classification for random dot product graphs. IEEE transactions on pattern analysis and machine intelligence, 36(1), 48-57. https://doi.org/10.1109/TPAMI.2013.135
Sussman, Daniel L, et al. "Consistent latent position estimation and vertex classification for random dot product graphs." IEEE transactions on pattern analysis and machine intelligence vol. 36,1 (2014): 48-57. doi: https://doi.org/10.1109/TPAMI.2013.135
Sussman DL, Tang M, Priebe CE. Consistent latent position estimation and vertex classification for random dot product graphs. IEEE Trans Pattern Anal Mach Intell. 2014 Jan;36(1):48-57. doi: 10.1109/TPAMI.2013.135. PMID: 24231865.
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IEEE Trans Pattern Anal Mach Intell. 2014 Jan;36(1):48-57. doi: 10.1109/TPAMI.2013.135.

Consistent latent position estimation and vertex classification for random dot product graphs.

IEEE transactions on pattern analysis and machine intelligence

Daniel L Sussman, Minh Tang, Carey E Priebe

Affiliations

  1. Johns Hopkins University, Baltimore.

PMID: 24231865 DOI: 10.1109/TPAMI.2013.135

Abstract

In this work, we show that using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the remaining vertices can be classified with error converging to Bayes optimal using the $(k)$-nearest-neighbors classification rule. We evaluate the proposed methods on simulated data and a graph derived from Wikipedia.

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