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Found 7 papers, 1 papers with code
Statistical Guarantees for Link Prediction using Graph Neural Networks
This paper derives statistical guarantees for the performance of Graph Neural Networks (GNNs) in link prediction tasks on graphs generated by a graphon.
A Local Graph Limits Perspective on Sampling-Based GNNs
We propose a theoretical framework for training Graph Neural Networks (GNNs) on large input graphs via training on small, fixed-size sampled subgraphs.
Locality-Aware Graph-Rewiring in GNNs
Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors.
Online Stochastic Max-Weight Bipartite Matching: Beyond Prophet Inequalities
This is the best possible ratio for this problem, as it generalizes the original single-item prophet inequality problem.
Data Structures and Algorithms
Sampling Arborescences in Parallel
For several well-studied combinatorial structures, counting can be reduced to the computation of a determinant, which is known to be in NC [Csa75].
Point Processes
Data Structures and Algorithms
Combinatorics
Probability
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons
We give an application to recovering assemblies of neurons.
Maximum Weight Online Matching with Deadlines
We study the problem of matching agents who arrive at a marketplace over time and leave after d time periods.
Data Structures and Algorithms
