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Adversarial contamination of networks in the setting of vertex
  nomination: a new trimming method

Adversarial contamination of networks in the setting of vertex nomination: a new trimming method

20 August 2022
Sheyda Peyman
M. Tang
V. Lyzinski
    AAML
ArXivPDFHTML

Papers citing "Adversarial contamination of networks in the setting of vertex nomination: a new trimming method"

14 / 14 papers shown
Title
Nonparametric Two-Sample Hypothesis Testing for Random Graphs with
  Negative and Repeated Eigenvalues
Nonparametric Two-Sample Hypothesis Testing for Random Graphs with Negative and Repeated Eigenvalues
Joshua Agterberg
M. Tang
Carey Priebe
37
15
0
17 Dec 2020
Adversarial Attack on Large Scale Graph
Adversarial Attack on Large Scale Graph
Jintang Li
Tao Xie
Liang Chen
Fenfang Xie
Xiangnan He
Zibin Zheng
AAML
50
67
0
08 Sep 2020
Vertex Nomination, Consistent Estimation, and Adversarial Modification
Vertex Nomination, Consistent Estimation, and Adversarial Modification
Joshua Agterberg
Youngser Park
Jonathan Larson
Christopher M. White
Carey E. Priebe
V. Lyzinski
83
16
0
06 May 2019
Statistical inference on random dot product graphs: a survey
Statistical inference on random dot product graphs: a survey
A. Athreya
D. E. Fishkind
Keith D. Levin
V. Lyzinski
Youngser Park
Yichen Qin
D. Sussman
M. Tang
Joshua T. Vogelstein
Carey E. Priebe
88
248
0
16 Sep 2017
The two-to-infinity norm and singular subspace geometry with
  applications to high-dimensional statistics
The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
Joshua Cape
M. Tang
Carey E. Priebe
54
132
0
30 May 2017
On the Consistency of the Likelihood Maximization Vertex Nomination
  Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph
  Matching
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
V. Lyzinski
Keith D. Levin
D. E. Fishkind
Carey E. Priebe
71
19
0
05 Jul 2016
Robust and computationally feasible community detection in the presence
  of arbitrary outlier nodes
Robust and computationally feasible community detection in the presence of arbitrary outlier nodes
T. Tony Cai
Xiaodong Li
141
140
0
23 Apr 2014
Hierarchical Block Structures and High-resolution Model Selection in
  Large Networks
Hierarchical Block Structures and High-resolution Model Selection in Large Networks
Tiago P. Peixoto
90
374
0
16 Oct 2013
Universally consistent vertex classification for latent positions graphs
Universally consistent vertex classification for latent positions graphs
M. Tang
D. Sussman
Carey E. Priebe
45
128
0
05 Dec 2012
Seeded Graph Matching
Seeded Graph Matching
D. E. Fishkind
Sancar Adali
Heather G. Patsolic
Lingyao Meng
Digvijay Singh
V. Lyzinski
Carey E. Priebe
44
77
0
03 Sep 2012
Pseudo-likelihood methods for community detection in large sparse
  networks
Pseudo-likelihood methods for community detection in large sparse networks
Arash A. Amini
Aiyou Chen
Peter J. Bickel
Elizaveta Levina
146
407
0
10 Jul 2012
Consistency of community detection in networks under degree-corrected
  stochastic block models
Consistency of community detection in networks under degree-corrected stochastic block models
Yunpeng Zhao
Elizaveta Levina
Ji Zhu
143
426
0
18 Oct 2011
A consistent adjacency spectral embedding for stochastic blockmodel
  graphs
A consistent adjacency spectral embedding for stochastic blockmodel graphs
D. Sussman
M. Tang
D. E. Fishkind
Carey E. Priebe
102
291
0
10 Aug 2011
Spectral clustering and the high-dimensional stochastic blockmodel
Spectral clustering and the high-dimensional stochastic blockmodel
Karl Rohe
S. Chatterjee
Bin Yu
186
931
0
09 Jul 2010
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