Optimized Quantization in Distributed Graph Signal Filtering

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We examine the connection of two graph parameters, the size of a minimum feedback arcs set and the acyclic disconnection. A feedback arc set of a directed graph is a subset of arcs such that after deletion the graph becomes acyclic. The acyclic disconnecti ...

Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms ...

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Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

Optimized Quantization in Distributed Graph Signal Filtering

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The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al

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Planetary-Scale Byzantine Fault Tolerance

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Equivariant Neural Architectures for Representing and Generating Graphs

Equilibria in Network Constrained Energy Markets

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

Scalable Computation of Dynamic Flow Problems via Multimarginal Graph-Structured Optimal Transport

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

Enforcing Privacy in Distributed Learning With Performance Guarantees

Hypergraph-Based Fast Distributed AC Power Flow Optimization

A full characterization of invariant embeddability of unimodular planar graphs

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Efficient and Accurate Handling of Periodic Flows in Time-Sensitive Networks

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Maximum Independent Set: Self-Training through Dynamic Programming

Discovering Influencers in Opinion Formation Over Social Graphs

Privacy-Preserving Machine Learning on Graphs