Results 1 to 10 of about 11,958 (208)
A Geometric Chung Lu model and the Drosophila Medulla connectome [PDF]
J. Complex Networks, 2021 Many real-world graphs have edges correlated to the distance between them, but in an inhomogeneous manner. While the Chung–Lu model and the geometric random graph models both are elegant in their simplicity, they are insufficient to capture the ...
S. Agarwala, Franklin Kenter
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Matching recovery threshold for correlated random graphs [PDF]
Annals of Statistics, 2022 For two correlated graphs which are independently sub-sampled from a common Erd\H{o}s-R\'enyi graph $\mathbf{G}(n, p)$, we wish to recover their \emph{latent} vertex matching from the observation of these two graphs \emph{without labels}.
Jian Ding, Hangyu Du
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What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding [PDF]
Neural Information Processing Systems, 2023 We aim to deepen the theoretical understanding of Graph Neural Networks (GNNs) on large graphs, with a focus on their expressive power. Existing analyses relate this notion to the graph isomorphism problem, which is mostly relevant for graphs of small ...
Nicolas Keriven, Samuel Vaiter
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Cliques in High-Dimensional Geometric Inhomogeneous Random Graphs [PDF]
International Colloquium on Automata, Languages and Programming, 2023 A recent trend in the context of graph theory is to bring theoretical analyses closer to empirical observations, by focusing the studies on random graph models that are used to represent practical instances.
T. Friedrich +3 more
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Threshold for detecting high dimensional geometry in anisotropic random geometric graphs [PDF]
Random Struct. Algorithms, 2022 In the anisotropic random geometric graph model, vertices correspond to points drawn from a high‐dimensional Gaussian distribution and two vertices are connected if their distance is smaller than a specified threshold.
Matthew Brennan +2 more
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Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis [PDF]
The Web Conference, 2021 In the analysis of large-scale network data, a fundamental operation is the comparison of observed phenomena to the predictions provided by null models: when we find an interesting structure in a family of real networks, it is important to ask whether ...
K. V. Koevering +2 more
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Modularity of the ABCD Random Graph Model with Community Structure [PDF]
J. Complex Networks, 2022 The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes.
B. Kamiński +3 more
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Risk Analysis, 2023 Networks like those of healthcare infrastructure have been a primary target of cyberattacks for over a decade. From just a single cyberattack, a healthcare facility would expect to see millions of dollars in losses from legal fines, business interruption,
Stefano Chiaradonna +2 more
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DropMessage: Unifying Random Dropping for Graph Neural Networks [PDF]
AAAI Conference on Artificial Intelligence, 2022 Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also face some challenges, such as over-fitting, over-smoothing, and non-robustness.
Taoran Fang +5 more
semanticscholar +1 more source
On the usefulness of graph-theoretic properties in the study of perceived numerosity
Behavior Research Methods, 2020 Observers can quickly estimate the quantity of sets of visual elements. Many aspects of this ability have been studied and the underlying system has been called the Approximate Number Sense (Dehaene, 2011).
M. Guest +3 more
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