Vince Lyzinski





2306 William E. Kirwan Hall
4176 Campus Dr
College Park, MD 20742

email: vlyzinsk (at sign) umd (dot) edu
[arXiv ID]
[Google Scholar]

Associate Professor, Statistics Program, Department of Mathematics
University of Maryland, College Park


Additional affiliations
Applied Mathematics, Statistics, and Scientific Computation (AMSC) program


Research interests

  • Statistical network inference
  • Graph matching (theory and algorithms)
  • Statistical machine learning
  • Markov chains
  • Probability
  • Combinatorics

  • Current and/or past research generously supported by DARPA, NIH and JHU HLTCOE
  • An overview of our work on the DARPA MAA program can be found here.

    My c.v. can be found here.
    My dissertation can be found here.


    Education

  • Ph.D. in Applied Mathematics and Statistics, Johns Hopkins University, 2013
    Advisor: Prof. James Allen Fill
    Dissertation title: Intertwinings, Interlacing Eigenvalues, and Strong Stationary Duality for Diffusions
  • M.S.E. in Applied Mathematics and Statistics, Johns Hopkins University, 2011
  • M.A. in Mathematics, Johns Hopkins University, 2007
  • B.S. in Mathematics, University of Notre Dame 2006
    Budapest Semesters in Mathematics, Spring 2005





  • My refereed journal publications can be (roughly) split into five main areas
  • Graph matching/network de-anonymization
  • Vertex nomination/IR on networks
  • Statistical network inference (clustering, testing, classification, etc.)
  • Markov chains/processes
  • Misc.

  • For refereed conference/workshop publications, click here
    1. Multiplex graph matching matched filters
      K. Pantazis, D. L. Sussman, Y. Park, Z. Li C. E. Priebe, V. Lyzinski
      Applied Network Science (2022), accepted for publication
      Link

    2. The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
      K. Pantazis, A. Athreya, J. Arroyo, W. N. Frost, E. S. Hill, V. Lyzinski
      Journal of Machine Learning Research (2022), accepted for publication
      Link

    3. Numerical Tolerance for Spectral Decompositions of Random Dot Product Graphs
      A. Athreya, M. Kane, B. Lewis, Z. Lubberts, V. Lyzinski, Y. Park, C. E. Priebe, M. Tang
      Journal of Computational and Graphical Statistics (2022), accepted for publication
      Link

    4. Vertex nomination between graphs via spectral embedding and quadratic programming
      R. Zheng, V. Lyzinski, C. E. Priebe, and M. Tang
      Journal of Computational and Graphical Statistics (2022), accepted for publication
      Link

    5. The Phantom Alignment Strength Conjecture: Practical use of graph matching alignment strength to indicate a meaningful graph match (previous title was "On Phantom Alignment Strength")
      D. E. Fishkind, F. Parker, H. Sawczuk, L. Meng, E. Bridgeford, A. Athreya, C. E. Priebe, and V. Lyzinski
      Applied Network Science, 6, Article number: 62 (2021)
      Link

    6. Graph matching between bipartite and unipartite networks: to collapse, or not to collapse, that is the question
      J. Arroyo, C. E. Priebe, V. Lyzinski
      IEEE Transactions on Network Science and Engineering, 8.4 (2021): 3019-3033
      Link, Code

    7. On a complete and sufficient statistic for the correlated Bernoulli random graph model
      D. E. Fishkind, A. Athreya, L. Meng, V. Lyzinski, C. E. Priebe
      Electronic Journal of Statistics, (2021) 15.1: 2336-2359
      Link

    8. Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
      J. Arroyo, D. L. Sussman, C. E. Priebe, V. Lyzinski
      Journal of Computational and Graphical Statistics, (2021), pp. 1-13, doi 10.1080/10618600.2021.1872582
      Link, Code

    9. Vertex Nomination, Consistent Estimation, and Adversarial Modification
      J. Agterberg, Y. Park, J. Larson, C. White, C. E. Priebe, V. Lyzinski
      Electronic Journal of Statistics (2020), vol. 14.2, pp. 3230-3267
      Link

    10. Vertex Nomination Via Seeded Graph Matching
      H. G. Patsolic, Y. Park, V. Lyzinski , C. E. Priebe
      Statistical Analysis and Data Mining (2020), vol. 13.3, pp. 229-244
      Link

    11. Vertex nomination: The canonical sampling and the extended spectral nomination schemes
      J. Yoder, L. Chen, H. Pao, E. Bridgeford, K. Levin, D. E. Fishkind, C. E. Priebe, V. Lyzinski
      Computational Statistics & Data Analysis (2020), vol. 145
      Link

    12. Matchability of heterogeneous networks pairs
      V. Lyzinski , D. L. Sussman
      Information and Inference: a Journal of the IMA 9.4 (2020): 749-783
      Link

    13. Matched filters for noisy induced subgraph detection
      D. L. Sussman, Y. Park, C. E. Priebe, V. Lyzinski
      IEEE Transcations on Pattern Analysis and Machine Intelligence vol. 42, no. 11, pp. 2887-2900, 1 Nov. 2020
      Link

    14. On Consistent Vertex Nomination Schemes
      V. Lyzinski, K. Levin, C.E. Priebe
      Journal of Machine Learning Research (2019), no. 69, pp. 1-39
      Link

    15. Neural Variational Entity Set Expansion for Automatically Populated Knowledge Graphs
      P. Rastogi, A. Poliak, V. Lyzinski, B. Van Durme
      Information Retrieval Journal (2019), 22.3-4, pp. 232-255
      [abstract can be found. here]

    16. On a 'Two-Truths' Phenomenon in Spectral Graph Clustering
      C. E. Priebe, Y. Park, J. T. Vogelstein, J. M . Conroy, V. Lyzinski, M. Tang, A. Athreya, J. Cape, E. Bridgeford
      Proceedings of the National Academy of Sciences (2019), 116.13, pp. 5995-6000
      Link

    17. Alignment strength and correlation for graphs
      D. E. Fishkind, L. Meng, A. Sun, C. E. Priebe, V. Lyzinski
      Pattern Recognition Letters (2019), 125, pp. 203--215
      Link

    18. Seeded Graph Matching
      D. E. Fishkind, S. Adali, H. G. Patsolic, L. Meng, D. Singh, V. Lyzinski, C.E. Priebe
      Pattern Recognition (2019), Vol. 87, pp. 203--215
      Link

    19. Statistical Inference on Random Dot Product Graphs: A Survey
      A. Athreya, D.E. Fishkind, K. Levin, V. Lyzinski, Y. Park, Y. Qin, D.L. Sussman, M. Tang, J.T. Vogelstein, C.E. and Priebe
      Journal of Machine Learning Research (2018), Vol. 18, pp. 1-92
      Link

    20. Information Recovery in Shuffled Graphs via Graph Matching
      V. Lyzinski
      IEEE Transactions on Information Theory (2018), 64(5), pp.3254-3273
      Link

    21. Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks
      A. Jansen, G. Sell, V. Lyzinski
      Pattern Recognition Letters (2017), Vol. 94(15), pp. 1-6
      Link

    22. Fast Embedding for JOFC Using the Raw Stress Criterion
      V. Lyzinski, Y. Park, C. E. Priebe and Michael Trosset
      Journal of Computational and Graphical Statistics (2017), 26(4), pp. 786-802
      Link

    23. A semiparametric two-sample hypothesis testing problem for random dot product graphs
      M. Tang, A. Athreya, D. L. Sussman, V. Lyzinski and C. E. Priebe
      Journal of Computational and Graphical Statistics (2017), 26(2), 344-354
      Link

    24. A nonparametric two-sample hypothesis testing problem for random dot product graphs
      M. Tang, A. Athreya, D. L. Sussman, V. Lyzinski and C. E. Priebe
      Bernoulli Journal (2017), Vol. 23 no. 3, 1599-1630
      Link

    25. Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements
      K. Levin and V. Lyzinski
      IEEE Transactions on Signal Processing (2017), Vol. 65(8)
      Link

    26. Community Detection and Classification in Hierarchical Stochastic Blockmodels
      V. Lyzinski, M. Tang, A. Athreya, Y. Park and C. E. Priebe
      IEEE Transactions on Network Science and Engineering (2017), 4(1), pp.13-26
      Link
      Data, code and figures are presented in more detail here

    27. Semi-External Memory Sparse Matrix Multiplication on Billion-node Graphs in a Multicore Architecture
      D. Zheng, D. Mhembere, V. Lyzinski, J. Vogelstein, C. E. Priebe, and R. Burns
      IEEE Transactions in Parallel and Distributed Systems (2017), Vol. 28(5), pp 1470-1483
      Link

    28. A limit theorem for scaled eigenvectors of random dot product graphs
      A. Athreya, C. E. Priebe, M. Tang, V. Lyzinski, D. J. Marchette and D. L. Sussman
      Sankhya A (2016), 78.1, pp 1-18
      Link

    29. A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes
      L. Chen, J. T. Vogelstein, V. Lyzinski, C. E. Priebe
      Worm (2016), 5(2)
      Link

    30. Graph Matching: Relax at Your Own Risk
      V. Lyzinski, D. E. Fishkind, M. Fiori, J. T. Vogelstein, C. E. Priebe and G. Sapiro
      IEEE Transactions on Pattern Analysis and Machine Intelligence (2016), 38(1), pp.60-73
      Link

    31. Strong Stationary Duality for Diffusion Processes
      J. A. Fill and V. Lyzinski
      Journal of Theoretical Probability (2016), Vol. 29(4), pp. 1298-1338
      Link

    32. On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
      V. Lyzinski, K. Levin, D. E. Fishkind and C. E. Priebe
      Journal of Machine Learning Research (2016), 17(179), pp.1-34
      Link

    33. Vertex Nomination Schemes for Membership Prediction
      D. E. Fishkind, V. Lyzinski, H. Pao, L. Chen and C. E. Priebe
      Annals of Applied Statistics (2015), 9.3 pp 1510-1532
      Link

    34. Logarithmic representability of integers as k-sums
      A. Godbole, S. Gutekunst, V. Lyzinski and Y. Zhuang
      Integers: The Electronic Journal of Combinatorial Number Theory (2015), vol. 15A: Proceedings of Integers 2013: The Erdős Centennial Conference
      Link

    35. Fast Approximate Quadratic Programming for Graph Matching
      J. T. Vogelstein, J. M. Conroy, V. Lyzinski, L. J. Podrazik, S. G. Kratzer, E. T. Harley, D. E. Fishkind, R. J. Vogelstein, C. E. Priebe
      PLOS One (2015), 10(4): e0121002. doi:10.1371/journal.pone.0121002
      Link

    36. Spectral Clustering for Divide-and-Conquer Graph Matching
      V. Lyzinski, D. Sussman, D. E. Fishkind, H. Pao, L. Chen, J. Vogelstein, Y. Park and C. E. Priebe
      Parallel Computing (2015), doi:10.1016/j.parco.2015.03.004
      Link

    37. Perfect clustering for stochastic block model graphs via adjacency spectral embedding
      V. Lyzinski, D. Sussman, M. Tang, A. Athreya and C. E. Priebe
      Electronic Journal of Statistics (2014), 8, pp 2905-2922
      Link

    38. Hitting times and interlacing eigenvalues: a stochastic approach using intertwinings
      J. A. Fill and V. Lyzinski
      Journal of Theoretical Probability (2014), Vol. 27(3), pp. 954-981
      Link

    39. Seeded graph matching for correlated Erdos-Renyi graphs
      V. Lyzinski, D. E. Fishkind and C. E. Priebe
      Journal of Machine Learning Research (2014), 15, pp 3513-3540
      Link

    40. Sharp threshold asymptotics for the emergence of additive bases
      A. Godbole, C. M. Lim, V. Lyzinski and N. Triantafillou
      Integers: The Electronic Journal of Combinatorial Number Theory (2013), vol. 13
      Link

    Refereed conference/workshop publications

    1. Multiplex graph matching matched filters
      K. Pantazis, D. L. Sussman, Y. Park, C. E. Priebe, V. Lyzinski
      GTA3 3.0: The 3rd workshop on Graph Techniques for Adversarial Activity Analytics, in Conjunction with the 2019 IEEE Big Data Conference, Los Angeles, CA, Dec 9, 2019

    2. Matched Filters for Noisy Induced Subgraph Detection
      D. Sussman, V. Lyzinski, Y. Park, C. E. Priebe
      GTA3 2018: Workshop on Graph Techniques for Adversarial Activity Analytics, in conjuction with 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, Feb 9, 2018
      (won the best paper award at the workshop)
      Link

    3. Metrics for Evaluating Network Alignment
      J. Douglas, B. Zimmerman, A. Kopylov, J. Xu, D. Sussman, V. Lyzinski
      GTA3 2018: Workshop on Graph Techniques for Adversarial Activity Analytics, in conjuction with 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, Feb 9, 2018
      Link

    4. A Central Limit Theorem for an Omnibus Embedding of Multiple Random Dot Product Graphs
      K. Levin, A. Avanti, M. Tang, V. Lyzinski, C. E. Priebe
      2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 964-967. IEEE
      Link

    5. An Evaluation of Graph Clustering Methods for Unsupervised Term Discovery
      V. Lyzinski, G. Sell, and A. Jansen
      Proceedings of Interspeech (2015), Dresden, Germany
      Link









    R code packages implementing our work can be downloaded from github at the following links. HTML web pages with downloadable code and worked examples can be found at the following links.




    Spring 2022

    Past Semesters (at UMD)

    Fall 2021: STAT426, DATA607
    Spring 2021: STAT650
    Winter 2020-2021: DATA607
    Fall 2020: STAT705
    Spring 2020: STAT426, STAT689 (RIT)
    Fall 2019: STAT426

    Past Semesters (at UMASS)

    Spring 2019: STAT516, STAT496 (Independent Study)
    Fall 2018: STAT697S
    Spring 2018: STAT516 (2 Sections), MATH596 (Independent Study)

    Past Semesters (at JHU)

    Fall 2016: 550.621
    Fall 2015: 550.620
    Spring 2014: 550.771
    Fall 2013: 550.310
    Summer 2013: 550.111
    Spring 2013: 550.111
    Fall 2012: 550.310
    Summer 2012: 550.230
    Fall 2011: 550.310
    Summer 2011: 550.230
    Spring 2011: 550.310
    Spring 2010: 550.310
    Summer 2009: 550.171
    Summer 2008: 550.111