1 code implementation • 6 Feb 2024 • Ariel Lubonja, Cencheng Shen, Carey Priebe, Randal Burns
New algorithms for embedding graphs have reduced the asymptotic complexity of finding low-dimensional representations.
no code implementations • 25 Oct 2022 • Kelly Marchisio, Ali Saad-Eldin, Kevin Duh, Carey Priebe, Philipp Koehn
Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval.
1 code implementation • Findings (EMNLP) 2021 • Kelly Marchisio, Youngser Park, Ali Saad-Eldin, Anton Alyakin, Kevin Duh, Carey Priebe, Philipp Koehn
Alternatively, word embeddings may be understood as nodes in a weighted graph.
no code implementations • 17 Dec 2020 • Joshua Agterberg, Minh Tang, Carey Priebe
We propose a nonparametric two-sample test statistic for low-rank, conditionally independent edge random graphs whose edge probability matrices have negative eigenvalues and arbitrarily close eigenvalues.
Graph Embedding Statistics Theory Statistics Theory
no code implementations • 12 Oct 2019 • Ian Gallagher, Andrew Jones, Anna Bertiger, Carey Priebe, Patrick Rubin-Delanchy
When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results.
no code implementations • 7 Jun 2019 • Hayden Helm, Joshua Vogelstein, Carey Priebe
This paper proposes a discrimination technique for vertices in a weighted network.
no code implementations • 4 Jun 2019 • Cencheng Shen, Li Chen, Yuexiao Dong, Carey Priebe
The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption.
no code implementations • 14 Feb 2018 • Jordan Yoder, Li Chen, Henry Pao, Eric Bridgeford, Keith Levin, Donniell Fishkind, Carey Priebe, Vince Lyzinski
There are vertex nomination schemes in the literature, including the optimally precise canonical nomination scheme~$\mathcal{L}^C$ and the consistent spectral partitioning nomination scheme~$\mathcal{L}^P$.
2 code implementations • 30 Dec 2014 • Heng Wang, Da Zheng, Randal Burns, Carey Priebe
A canonical problem in graph mining is the detection of dense communities.
Social and Information Networks Physics and Society
no code implementations • 24 Jun 2014 • Nam H. Lee, Carey Priebe, Youngser Park, I-Jeng Wang, Michael Rosen
A natural approach to analyze interaction data of form "what-connects-to-what-when" is to create a time-series (or rather a sequence) of graphs through temporal discretization (bandwidth selection) and spatial discretization (vertex contraction).
no code implementations • 23 Nov 2013 • Li Chen, Cencheng Shen, Joshua Vogelstein, Carey Priebe
For random graphs distributed according to stochastic blockmodels, a special case of latent position graphs, adjacency spectral embedding followed by appropriate vertex classification is asymptotically Bayes optimal; but this approach requires knowledge of and critically depends on the model dimension.
no code implementations • 2 Oct 2013 • Vince Lyzinski, Daniel Sussman, Minh Tang, Avanti Athreya, Carey Priebe
Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research.