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Found 4 papers, 1 papers with code
Matching Correlated Inhomogeneous Random Graphs using the $k$-core Estimator
We consider the task of estimating the latent vertex correspondence between two edge-correlated random graphs with generic, inhomogeneous structure.
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
This renders the features amenable to train a variety of classifiers to perform causal inference.
Exact Community Recovery in Correlated Stochastic Block Models
In particular, we uncover and characterize a region of the parameter space where exact community recovery is possible using multiple correlated graphs, even though (1) this is information-theoretically impossible using a single graph and (2) exact graph matching is also information-theoretically impossible.
Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities
We consider the task of learning latent community structure from multiple correlated networks.
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