Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Vince Lyzinski"'
Autor:
Konstantinos Pantazis, Daniel L. Sussman, Youngser Park, Zhirui Li, Carey E. Priebe, Vince Lyzinski
Publikováno v:
Applied Network Science, Vol 7, Iss 1, Pp 1-35 (2022)
Abstract We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the graph matching matched filter framework of Sussman et al. (IEEE Trans Pattern Anal Mach
Externí odkaz:
https://doaj.org/article/4c617ab3e800468f8036b9d7ecd01668
Autor:
Donniell E. Fishkind, Felix Parker, Hamilton Sawczuk, Lingyao Meng, Eric Bridgeford, Avanti Athreya, Carey Priebe, Vince Lyzinski
Publikováno v:
Applied Network Science, Vol 6, Iss 1, Pp 1-27 (2021)
Abstract The alignment strength of a graph matching is a quantity that gives the practitioner a measure of the correlation of the two graphs, and it can also give the practitioner a sense for whether the graph matching algorithm found the true matchi
Externí odkaz:
https://doaj.org/article/217e4cb8070b46ea848f268eed58bd88
Autor:
Joshua T Vogelstein, John M Conroy, Vince Lyzinski, Louis J Podrazik, Steven G Kratzer, Eric T Harley, Donniell E Fishkind, R Jacob Vogelstein, Carey E Priebe
Publikováno v:
PLoS ONE, Vol 10, Iss 4, p e0121002 (2015)
Quadratic assignment problems arise in a wide variety of domains, spanning operations research, graph theory, computer vision, and neuroscience, to name a few. The graph matching problem is a special case of the quadratic assignment problem, and grap
Externí odkaz:
https://doaj.org/article/7616ba0fe37c4639baa030d63dbb392c
Autor:
Avanti Athreya, Zachary Lubberts, Carey E. Priebe, Youngser Park, Minh Tang, Vince Lyzinski, Michael Kane, Bryan W. Lewis
Publikováno v:
Journal of Computational and Graphical Statistics. 32:145-156
Publikováno v:
IEEE Trans Netw Sci Eng
Graph matching consists of aligning the vertices of two unlabeled graphs in order to maximize the shared structure across networks; when the graphs are unipartite, this is commonly formulated as minimizing their edge disagreements. In this paper, we
Publikováno v:
Journal of Computational and Graphical Statistics. 30:1111-1123
Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements. We study this problem
Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network. We consider matching the shuffled
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a15951608f35e251482d90222d605d2
This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting subgraphs. This type of problem appears time and again in real world problems connected to, for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3113ed1c063b0dba364207bd196147a
http://arxiv.org/abs/2101.12430
http://arxiv.org/abs/2101.12430
Autor:
Eric W. Bridgeford, Avanti Athreya, Vince Lyzinski, Hamilton Sawczuk, Felix Parker, Lingyao Meng, Carey E. Priebe, Donniell E. Fishkind
Publikováno v:
Applied Network Science, Vol 6, Iss 1, Pp 1-27 (2021)
The alignment strength of a graph matching is a quantity that gives the practitioner a measure of the correlation of the two graphs, and it can also give the practitioner a sense for whether the graph matching algorithm found the true matching. Unfor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa4adf310a26e6eda818a681388d60a8
Publikováno v:
Electronic Journal of Statistics. 15
Inference on vertex-aligned graphs is of wide theoretical and practical importance.There are, however, few flexible and tractable statistical models for correlated graphs, and even fewer comprehensive approaches to parametric inference on data arisin