Zobrazeno 1 - 10
of 199
pro vyhledávání: '"Lyzinski, Vince"'
Autor:
Pantazis, Konstantinos, Trosset, Michael, Frost, William N., Priebe, Carey E., Lyzinski, Vince
Theoretical and empirical evidence suggests that joint graph embedding algorithms induce correlation across the networks in the embedding space. In the Omnibus joint graph embedding framework, previous results explicitly delineated the dual effects o
Externí odkaz:
http://arxiv.org/abs/2409.17544
Autor:
Leinwand, Benjamin, Lyzinski, Vince
Modeling networks can serve as a means of summarizing high-dimensional complex systems. Adapting an approach devised for dense, weighted networks, we propose a new method for generating and estimating unweighted networks. This approach can describe a
Externí odkaz:
http://arxiv.org/abs/2404.07462
Autor:
Qi, Tong, Lyzinski, Vince
In this paper, we explore the capability of both the Adjacency Spectral Embedding (ASE) and the Graph Encoder Embedding (GEE) for capturing an embedded pseudo-clique structure in the random dot product graph setting. In both theory and experiments, w
Externí odkaz:
http://arxiv.org/abs/2312.11054
The joint analysis of multimodal neuroimaging data is critical in the field of brain research because it reveals complex interactive relationships between neurobiological structures and functions. In this study, we focus on investigating the effects
Externí odkaz:
http://arxiv.org/abs/2310.18533
We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph. Our method builds upon the graph-matching-matched-filter technique proposed in Sussman et al., with the discovery of multiple diverse
Externí odkaz:
http://arxiv.org/abs/2308.13451
The subgraph-subgraph matching problem is, given a pair of graphs and a positive integer $K$, to find $K$ vertices in the first graph, $K$ vertices in the second graph, and a bijection between them, so as to minimize the number of adjacency disagreem
Externí odkaz:
http://arxiv.org/abs/2306.04016
As graph data becomes more ubiquitous, the need for robust inferential graph algorithms to operate in these complex data domains is crucial. In many cases of interest, inference is further complicated by the presence of adversarial data contamination
Externí odkaz:
http://arxiv.org/abs/2208.09710
Autor:
Saxena, Ayushi, Lyzinski, Vince
Two-sample network hypothesis testing is an important inference task with applications across diverse fields such as medicine, neuroscience, and sociology. Many of these testing methodologies operate under the implicit assumption that the vertex corr
Externí odkaz:
http://arxiv.org/abs/2208.08638
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:
http://arxiv.org/abs/2205.03486
Autor:
Milzman, Jesse, Lyzinski, Vince
We investigate the partial information decomposition (PID) framework as a tool for edge nomination. We consider both the $I_{\cap}^{\text{min}}$ and $I_{\cap}^{\text{PM}}$ PIDs, from arXiv:1004.2515 and arXiv:1801.09010 respectively, and we both nume
Externí odkaz:
http://arxiv.org/abs/2112.12316