Results 1 to 10 of about 5,903,601 (180)
Filtering Random Graph Processes Over Random Time-Varying Graphs [PDF]
IEEE Transactions on Signal Processing, 2017 Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochastic- ity in both the graph topology as well as the signal itself.
Isufi, E. (author) +3 more
openaire +6 more sources
hergm: Hierarchical Exponential-Family Random Graph Models
Journal of Statistical Software, 2018 We describe the R package hergm that implements hierarchical exponential-family random graph models with local dependence. Hierarchical exponential-family random graph models with local dependence tend to be superior to conventional exponential-family ...
Michael Schweinberger, Pamela Luna
doaj +2 more sources
Directed network Laplacians and random graph models [PDF]
Royal Society Open Science, 2021 We consider spectral methods that uncover hidden structures in directed networks. We establish and exploit connections between node reordering via (a) minimizing an objective function and (b) maximizing the likelihood of a random graph model. We focus on
Xue Gong +2 more
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Settling the Sharp Reconstruction Thresholds of Random Graph Matching [PDF]
International Symposium on Information Theory, 2021 This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs. We focus on the Gaussian model where the two graphs are complete graphs with correlated Gaussian weights and the Erdős-Rényi model ...
Yihong Wu, Jiaming Xu, Sophie H. Yu
semanticscholar +1 more source
Local dependence in random graph models: characterization, properties and statistical inference. [PDF]
J Am Stat Assoc, 2015 Schweinberger M, Handcock MS.
europepmc +2 more sources
Random graph asymptotics for treatment effect estimation under network interference [PDF]
Annals of Statistics, 2020 The network interference model for causal inference places all experimental units at the vertices of an undirected exposure graph, such that treatment assigned to one unit may affect the outcome of another unit if and only if these two units are ...
Shuangning Li, Stefan Wager
semanticscholar +1 more source
The Spectral Distribution of Random Mixed Graphs
Axioms, 2022 In this work, we propose a random mixed graph model Gn(p(n),q(n)) that incorporates both the classical Erdős-Rényi’s random graph model and the random oriented graph model.
Yue Guan +7 more
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Randomized graph cluster randomization
Journal of Causal Inference, 2023 Abstract The global average treatment effect (GATE) is a primary quantity of interest in the study of causal inference under network interference. With a correctly specified exposure model of the interference, the Horvitz–Thompson (HT) and Hájek estimators of the GATE are unbiased and consistent, respectively, yet known to exhibit ...
Ugander Johan, Yin Hao
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Degree distribution in random planar graphs [PDF]
Discrete Mathematics & Theoretical Computer Science, 2008 We prove that for each $k \geq 0$, the probability that a root vertex in a random planar graph has degree $k$ tends to a computable constant $d_k$, and moreover that $\sum_k d_k =1$. The proof uses the tools developed by Gimènez and Noy in their solution
Michael Drmota, Omer Gimenez, Marc Noy
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Rigorous Result for the CHKNS Random Graph Model [PDF]
Discrete Mathematics & Theoretical Computer Science, 2003 We study the phase transition in a random graph in which vertices and edges are added at constant rates. Two recent papers in Physical Review E by Callaway, Hopcroft, Kleinberg, Newman, and Strogatz, and Dorogovstev, Mendes, and Samukhin have computed ...
Rick Durrett
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