Analysis of the Limits of Graph-Based Object Duplicate Detection

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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, ...

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 ...

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 ...

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 ...

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 ...

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

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 ...

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 ...

This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noise, through the process of adding or ...

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

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 ...

In this paper we propose a novel virtual simulation-pilot engine for speeding up air traffic controller (ATCo) training by integrating different state-of-the-art artificial intelligence (AI)-based tools. The virtual simulation-pilot engine receives spoken ...

Machine learning has paved the way for the real-time monitoring of complex infrastructure and industrial systems. However, purely data-driven methods have not been able to learn the underlying dynamics and generalize them to operating conditions that have ...

An integer linear program is a problem of the form max{c^T x : Ax=b, x >= 0, x integer}, where A is in Z^(n x m), b in Z^m, and c in Z^n. Solving an integer linear program is NP-hard in general, but there are several assumptions for which it becomes fixed ...

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In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...

Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that live ...

Community structure in graph-modeled data appears in a range of disciplines that comprise network science. Its importance relies on the influence it bears on other properties of graphs such as resilience, or prediction of missing connections. Nevertheless, ...

This paper proposes a versatile mapping approach that has three objectives: i) it can map from one technology-independent graph representation to another; ii) it can map to a cell library; iii) it supports logic rewriting. The method is cut-based, mitigate ...

We consider the problem of learning implicit neural representations (INRs) for signals on non-Euclidean domains. In the Euclidean case, INRs are trained on a discrete sampling of a signal over a regular lattice. Here, we assume that the continuous signal e ...

Analysis of the Limits of Graph-Based Object Duplicate Detection

Chat with Graph Search

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

Equivariant Neural Architectures for Representing and Generating Graphs

Privacy-Preserving Machine Learning on Graphs

Privatized graph federated learning

A full characterization of invariant embeddability of unimodular planar graphs

Scalable maximal subgraph mining with backbone-preserving graph convolutions

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

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

DiGress: Discrete Denoising diffusion for graph generation

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Discovering Influencers in Opinion Formation Over Social Graphs

A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers

Graph neural networks for dynamic modeling of roller bearings

Results on Sparse Integer Programming and Geometric Independent Sets

Graph Embedding for Retrieval

Graph Representation Learning with Optimal Transport: Analysis and Applications

Distributed Graph Learning With Smooth Data Priors

Graph signal processing tailored for subgraph focus and community structure

A Versatile Mapping Approach for Technology Mapping and Graph Optimization

Generalised Implicit Neural Representations