Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Athreya, Avanti"'
We describe a model for a network time series whose evolution is governed by an underlying stochastic process, known as the latent position process, in which network evolution can be represented in Euclidean space by a curve, called the Euclidean mir
Externí odkaz:
http://arxiv.org/abs/2405.11111
Autor:
Chen, Tianyi, Park, Youngser, Saad-Eldin, Ali, Lubberts, Zachary, Athreya, Avanti, Pedigo, Benjamin D., Vogelstein, Joshua T., Puppo, Francesca, Silva, Gabriel A., Muotri, Alysson R., Yang, Weiwei, White, Christopher M., Priebe, Carey E.
Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring the effe
Externí odkaz:
http://arxiv.org/abs/2303.04871
Analyzing changes in network evolution is central to statistical network inference, as underscored by recent challenges of predicting and distinguishing pandemic-induced transformations in organizational and communication networks. We consider a join
Externí odkaz:
http://arxiv.org/abs/2205.06877
We extend the latent position random graph model to the line graph of a random graph, which is formed by creating a vertex for each edge in the original random graph, and connecting each pair of edges incident to a common vertex in the original graph
Externí odkaz:
http://arxiv.org/abs/2103.14726
Autor:
Fishkind, Donniell E., Parker, Felix, Sawczuk, Hamilton, Meng, Lingyao, Bridgeford, Eric, Athreya, Avanti, Priebe, Carey E., Lyzinski, Vince
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:
http://arxiv.org/abs/2103.00624
Autor:
Chen, Guodong, Arroyo, Jesús, Athreya, Avanti, Cape, Joshua, Vogelstein, Joshua T., Park, Youngser, White, Chris, Larson, Jonathan, Yang, Weiwei, Priebe, Carey E.
Publikováno v:
In Computational Statistics and Data Analysis March 2025 203
Autor:
Chen, Guodong, Arroyo, Jesús, Athreya, Avanti, Cape, Joshua, Vogelstein, Joshua T., Park, Youngser, White, Chris, Larson, Jonathan, Yang, Weiwei, Priebe, Carey E.
This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally ano
Externí odkaz:
http://arxiv.org/abs/2008.10055
Autor:
Pantazis, Konstantinos, Athreya, Avanti, Arroyo, Jesús, Frost, William N., Hill, Evan S., Lyzinski, Vince
Spectral inference on multiple networks is a rapidly-developing subfield of graph statistics. Recent work has demonstrated that joint, or simultaneous, spectral embedding of multiple independent networks can deliver more accurate estimation than indi
Externí odkaz:
http://arxiv.org/abs/2008.00163
In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity. Beyond mere adjacency matrices, many real networks also involve vertex
Externí odkaz:
http://arxiv.org/abs/2007.02156
Autor:
Helm, Hayden S., Basu, Amitabh, Athreya, Avanti, Park, Youngser, Vogelstein, Joshua T., Priebe, Carey E., Winding, Michael, Zlatic, Marta, Cardona, Albert, Bourke, Patrick, Larson, Jonathan, Abdin, Marah, Choudhury, Piali, Yang, Weiwei, White, Christopher W.
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest. The setting we consider is one in which no analytic description of what constitutes a good ra
Externí odkaz:
http://arxiv.org/abs/2005.10700