Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Sussman, Daniel L."'
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
Autor:
Kung, Kelly, Sussman, Daniel L.
Causal inference methods have been applied in various fields where researchers want to estimate treatment effects. In traditional causal inference settings, one assumes that the outcome of a unit does not depend on treatments of other units. However,
Externí odkaz:
http://arxiv.org/abs/2209.01053
Increasingly, there is a marked interest in estimating causal effects under network interference due to the fact that interference manifests naturally in networked experiments. However, network information generally is available only up to some level
Externí odkaz:
http://arxiv.org/abs/2105.04518
Autor:
Draves, Benjamin, Sussman, Daniel L.
Joint spectral embeddings facilitate analysis of multiple network data by simultaneously mapping vertices in each network to points in Euclidean space where statistical inference is then performed. In this work, we consider one such joint embedding t
Externí odkaz:
http://arxiv.org/abs/2005.02511
Many fundamental concepts in network-based epidemic modeling depend on the branching factor, which captures a sense of dispersion in the network connectivity and quantifies the rate of spreading across the network. Moreover, contact network informati
Externí odkaz:
http://arxiv.org/abs/2002.05763
Autor:
Pantazis, Konstantinos, Sussman, Daniel L., Park, Youngser, Li, Zhirui, Priebe, Carey E., Lyzinski, Vince
We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the framework of Sussman et al. (2019) to the multiplex setting, leverages a multiplex analogue of t
Externí odkaz:
http://arxiv.org/abs/1908.02572
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
Externí odkaz:
http://arxiv.org/abs/1812.10519
The graph matching problem aims to discover a latent correspondence between the vertex sets of two observed graphs. This problem has proven to be quite challenging, with few satisfying methods that are computationally tractable and widely applicable.
Externí odkaz:
http://arxiv.org/abs/1807.09299
The problem of finding the vertex correspondence between two noisy graphs with different number of vertices where the smaller graph is still large has many applications in social networks, neuroscience, and computer vision. We propose a solution to t
Externí odkaz:
http://arxiv.org/abs/1803.02423
Autor:
Athreya, Avanti, Fishkind, Donniell E., Levin, Keith, Lyzinski, Vince, Park, Youngser, Qin, Yichen, Sussman, Daniel L., Tang, Minh, Vogelstein, Joshua T., Priebe, Carey E.
Publikováno v:
Journal of Machine Learning Research, 2018
The random dot product graph (RDPG) is an independent-edge random graph that is analytically tractable and, simultaneously, either encompasses or can successfully approximate a wide range of random graphs, from relatively simple stochastic block mode
Externí odkaz:
http://arxiv.org/abs/1709.05454