Mihai Cucuringu

research

∙ 08/01/2023

Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects

We present a novel methodology for modeling and forecasting multivariate...

0 Chao Zhang, et al. ∙

research

∙ 06/29/2023

SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation

Over recent years, denoising diffusion generative models have come to be...

0 Stratis Limnios, et al. ∙

research

∙ 05/25/2023

The GNAR-edge model: A network autoregressive model for networks with time-varying edge weights

In economic and financial applications, there is often the need for anal...

0 Anastasia Mantziou, et al. ∙

research

∙ 05/11/2023

Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models

In multivariate time series systems, key insights can be obtained by dis...

0 Yichi Zhang, et al. ∙

research

∙ 04/08/2023

OFTER: An Online Pipeline for Time Series Forecasting

We introduce OFTER, a time series forecasting pipeline tailored for mid-...

0 Nikolas Michael, et al. ∙

research

∙ 12/01/2022

Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models

This paper revisits building machine learning algorithms that involve in...

0 Qiong Wu, et al. ∙

research

∙ 09/01/2022

MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian

Signed and directed networks are ubiquitous in real-world applications. ...

0 Yixuan He, et al. ∙

research

∙ 03/29/2022

Graph similarity learning for change-point detection in dynamic networks

Dynamic networks are ubiquitous for modelling sequential graph-structure...

9 Déborah Sulem, et al. ∙

research

∙ 03/28/2022

DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series

In this work, we introduce DAMNETS, a deep generative model for Markovia...

0 Jase Clarkson, et al. ∙

research

∙ 02/22/2022

PyTorch Geometric Signed Directed: A Survey and Software on Graph Neural Networks for Signed and Directed Graphs

Signed networks are ubiquitous in many real-world applications (e.g., so...

0 Yixuan He, et al. ∙

research

∙ 02/01/2022

GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks

Recovering global rankings from pairwise comparisons is an important pro...

0 Yixuan He, et al. ∙

research

∙ 01/23/2022

Option Volume Imbalance as a predictor for equity market returns

We investigate the use of the normalized imbalance between option volume...

0 Nikolas Michael, et al. ∙

research

∙ 01/20/2022

Lead-lag detection and network clustering for multivariate time series with an application to the US equity market

In multivariate time series systems, it has been observed that certain g...

0 Stefanos Bennett, et al. ∙

research

∙ 01/12/2022

Local2Global: A distributed approach for scaling representation learning on graphs

We propose a decentralised "local2global"' approach to graph representat...

34 Lucas G. S. Jeub, et al. ∙

research

∙ 10/13/2021

SSSNET: Semi-Supervised Signed Network Clustering

Node embeddings are a powerful tool in the analysis of networks; yet, th...

0 Yixuan He, et al. ∙

research

∙ 07/26/2021

Local2Global: Scaling global representation learning on graphs via local training

We propose a decentralised "local2global" approach to graph representati...

2 Lucas G. S. Jeub, et al. ∙

research

∙ 07/02/2021

DUKweb: Diachronic word representations from the UK Web Archive corpus

Lexical semantic change (detecting shifts in the meaning and usage of wo...

0 Adam Tsakalidis, et al. ∙

research

∙ 06/09/2021

DIGRAC: Digraph Clustering with Flow Imbalance

Node clustering is a powerful tool in the analysis of networks. Here, we...

0 Yixuan He, et al. ∙

research

∙ 12/29/2020

An extension of the angular synchronization problem to the heterogeneous setting

Given an undirected measurement graph G = ([n], E), the classical angula...

4 Mihai Cucuringu, et al. ∙

research

∙ 11/03/2020

Regularized spectral methods for clustering signed networks

We study the problem of k-way clustering in signed graphs. Considerable ...

6 Mihai Cucuringu, et al. ∙

research

∙ 09/23/2020

A Linear Transportation L^p Distance for Pattern Recognition

The transportation L^p distance, denoted TL^p, has been proposed as a ge...

11 Oliver M. Crook, et al. ∙

research

∙ 05/08/2020

Spectral Ranking with Covariates

We consider approaches to the classical problem of establishing a statis...

5 Siu Lun Chau, et al. ∙

research

∙ 04/04/2020

Learning with Semi-Definite Programming: new statistical bounds based on fixed point analysis and excess risk curvature

Many statistical learning problems have recently been shown to be amenab...

0 Stéphane Chrétien, et al. ∙

research

∙ 04/02/2020

Motif-Based Spectral Clustering of Weighted Directed Networks

Clustering is an essential technique for network analysis, with applicat...

0 William George Underwood, et al. ∙

research

∙ 12/02/2019

Core-Periphery Structure in Directed Networks

While studies of meso-scale structures in networks often focus on commun...

0 Andrew Elliott, et al. ∙

research

∙ 09/07/2019

A Deep Learning Framework for Pricing Financial Instruments

We propose an integrated deep learning architecture for the stock moveme...

0 Qiong Wu, et al. ∙

research

∙ 08/06/2019

Hermitian matrices for clustering directed graphs: insights and applications

Graph clustering is a basic technique in machine learning, and has wides...

4 Mihai Cucuringu, et al. ∙

research

∙ 06/06/2019

Ranking and synchronization from pairwise measurements via SVD

Given a measurement graph G= (V,E) and an unknown signal r ∈R^n, we inve...

0 Alexandre d'Aspremont, et al. ∙

research

∙ 04/18/2019

SPONGE: A generalized eigenproblem for clustering signed networks

We introduce a principled and theoretically sound spectral method for k-...

0 Mihai Cucuringu, et al. ∙

research

∙ 01/10/2019

An MBO scheme for clustering and semi-supervised clustering of signed networks

We introduce a principled method for the signed clustering problem, wher...

0 Mihai Cucuringu, et al. ∙

research

∙ 01/02/2019

Anomaly Detection in Networks with Application to Financial Transaction Networks

This paper is motivated by the task of detecting anomalies in networks o...

0 Andrew Elliott, et al. ∙

research

∙ 07/04/2018

Modeling outcomes of soccer matches

We compare various extensions of the Bradley-Terry model and a hierarchi...

0 Alkeos Tsokos, et al. ∙

research

∙ 03/09/2018

Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping

Consider an unknown smooth function f: [0,1]^d →R, and say we are given ...

0 Mihai Cucuringu, et al. ∙

research

∙ 10/27/2017

On denoising modulo 1 samples of a function

Consider an unknown smooth function f: [0,1] →R, and say we are given n ...

0 Mihai Cucuringu, et al. ∙

research

∙ 04/05/2015

Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and Semidefinite Programming Synchronization

We consider the classic problem of establishing a statistical ranking of...

0 Mihai Cucuringu, et al. ∙

research

∙ 04/03/2015

Point Localization and Density Estimation from Ordinal kNN graphs using Synchronization

We consider the problem of embedding unweighted, directed k-nearest neig...

0 Mihai Cucuringu, et al. ∙

research

∙ 11/02/2011

Model Selection in Undirected Graphical Models with the Elastic Net

Structure learning in random fields has attracted considerable attention...

0 Mihai Cucuringu, et al. ∙

Mihai Cucuringu

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