Toward a Multilevel Cognitive Probabilistic Representation of Space

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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 ...

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 ...

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based ...

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interact ...

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation functio ...

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

Many sports leagues organize their competitions as round-robin tournaments. This tournament design has a rich mathematical structure that has been studied in the literature over the years. We review some of the main properties and fundamental scheduling me ...

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 ...

In this work, we develop a new framework for dynamic network flow pro-blems based on optimal transport theory. We show that the dynamic multicommodity minimum-cost network flow problem can be formulated as a multimarginal optimal transport problem, where t ...

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

This paper considers the problem of second-degree price discrimination when the type distribution is unknown or imperfectly specified by means of an ambiguity set. As robustness measure we use a performance index, equivalent to relative regret, which quant ...

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...

When can a unimodular random planar graph be drawn in the Euclidean or the hyperbolic plane in a way that the distribution of the random drawing is isometry-invariant? This question was answered for one-ended unimodular graphs in Benjamini and Timar, using ...

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

Toward a Multilevel Cognitive Probabilistic Representation of Space

Chattez avec Graph Search

The Power of Two Matrices in Spectral Algorithms for Community Recovery

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

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

Molecular hypergraph neural networks

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Network-based kinetic models: Emergence of a statistical description of the graph topology

Equivariant Neural Architectures for Representing and Generating Graphs

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

Discovering Influencers in Opinion Formation Over Social Graphs

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

A tutorial on graph models for scheduling round-robin sports tournaments

Privacy-Preserving Machine Learning on Graphs

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

Privatized graph federated learning

Price Discrimination with Robust Beliefs

Enforcing Privacy in Distributed Learning With Performance Guarantees

Maximum Independent Set: Self-Training through Dynamic Programming

A full characterization of invariant embeddability of unimodular planar graphs

Equilibria in Network Constrained Energy Markets