On Phantom Alignment Strength
D. E. Fishkind, F. Parker, H. Sawczuk, L. Meng, E. Bridgeford, A. Athreya, C. E. Priebe, and V. Lyzinski
arXiv 2103.00624 (2021)
Link
Subgraph nomination: Query by Example Subgraph Retrieval in Networks
A.-F. M. Al-Qadhi, C. E. Priebe, H. S. Helm, and V. Lyzinski
arXiv 2101.12430 (2021)
Link
Vertex nomination between graphs via spectral embedding and quadratic programming
R. Zheng, V. Lyzinski, C. E. Priebe, and M. Tang
arXiv 2010.14622 (2020)
Link
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
K. Pantazis, A. Athreya, W. N. Frost, E. S. Hill, V. Lyzinski
arXiv 2008.00163 (2020)
Link
On the role of features in vertex nomination: Content and context together are better (sometimes)
K. Levin, C. E. Priebe, V. Lyzinski
arXiv 2005.02151 (2020)
Link
Multiplex graph matching matched filters
K. Pantazis, D. L. Sussman, Y. Park, C. E. Priebe, V. Lyzinski
arXiv 1908.02572 (2019)
Link
Tractable Graph Matching via Soft Seeding
F. Fang, D. L. Sussman, V. Lyzinski
arXiv 1807.09299 (2018)
Link
Semiparametric spectral modeling of the Drosophila connectome
C. E. Priebe, Y. Park, M. Tang, A. Athreya, V. Lyzinski, J. T. Vogelstein, Y. Qin, B. Cocanougher, K. Eichler, M. Zlatic, A. Cardona
arXiv 1705.03297 (2017)
Link
A central limit theorem for an omnibus embedding of random dot product graphs
K. Levin, A. Athreya, M. Tang, V. Lyzinski, C.E. Priebe
arXiv 1705.09355, (2017)
Link
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. Tan
arXiv 1608.00451 (2016, revision submitted 2020)
Link
Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability
H. Patsolic, S. Adali, J. Vogelstein, Y. Park, C. E. Priebe, G. Li, V. Lyzinski
arXiv 1401.3813 (2014, revision submitted 2019)
Link
Graph matching/network de-anonymization
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, accepted for publication, (2021)
Link
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, accepted for publication, (2021)
Link
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
Matchability of heterogeneous networks pairs
V. Lyzinski , D. L. Sussman
Information and Inference: a Journal of the IMA (2020), az031, https://doi.org/10.1093/imaiai/iaz031
Link
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 (2019+), to appear
Link
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
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
Information Recovery in Shuffled Graphs via Graph Matching
V. Lyzinski
IEEE Transactions on Information Theory (2018), 64(5), pp.3254-3273
Link
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
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
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
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
Vertex nomination/IR on networks
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
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
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
On Consistent Vertex Nomination Schemes
V. Lyzinski, K. Levin, C.E. Priebe
Journal of Machine Learning Research (2019), no. 69, pp. 1-39
Link
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]
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
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
Random graph inference (clustering, testing, classification, etc.)
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
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
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
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
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
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
Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements
K. Levin and V. Lyzinski
IEEE Transactions on Signal Processing (2017), Vol. 65(8)
Link
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
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
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
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
Markov chains/processes
Strong Stationary Duality for Diffusion Processes
J. A. Fill and V. Lyzinski
Journal of Theoretical Probability (2016), Vol. 29(4), pp. 1298-1338
Link
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
Misc.
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
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
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
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
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
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
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
An Evaluation of Graph Clustering Methods for Unsupervised Term Discovery
V. Lyzinski, G. Sell, and A. Jansen
Proceedings of Interspeech (2015), Dresden, Germany
Link
iGraphMatch: Tools to find the correspondences between vertices in different graphs.
This package implements our graph matching code base (and numerous other graph matching algorithms from the literature). Work and maintenance are funded (in part) by my DARPA MAA awards (award numbers FA8750-20-2-1001 and FA8750-18-0035).
Update: Now on CRAN! here.
Earlier versions of our graph matching code can be found here, and in the igraph R package (the match_vertices function)
VN: Implements the algorithms from
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
Code and worked out examples can be found here
Fast Embedding for JOFC Using the Raw Stress Criterion
(supplemental code also available at publisher's site here)
Semiparametric spectral modeling of the Drosophila connectome (preprint)
(code reproduces figures also found in our JMLR survey paper)