41 citations · 95 across the 19 of their papers we have counts for
6 papers · 1 filter
Trade Co-occurrence, Trade Flow Decomposition, and Conditional Order Imbalance in Equity Markets
Yutong Lu, Gesine Reinert, Mihai Cucuringu
The time proximity of high-frequency trades can contain a salient signal. In this paper, we propose a method to classify every trade, based on its proximity with other trades in th…
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
Yixuan He, Michael Permultter, Gesine Reinert +1
Signed and directed networks are ubiquitous in real-world applications. However, there has been relatively little work proposing spectral graph neural networks (GNNs) for such netw…
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
Jase Clarkson, Mihai Cucuringu, Andrew Elliott +1
Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based…
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
Yixuan He, Xitong Zhang, Junjie Huang +3
Networks are ubiquitous in many real-world applications (e.g., social networks encoding trust/distrust relationships, correlation networks arising from time series data). While man…
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
Yixuan He, Quan Gan, David Wipf +3
Recovering global rankings from pairwise comparisons has wide applications from time synchronization to sports team ranking. Pairwise comparisons corresponding to matches in a comp…
Lead-lag detection and network clustering for multivariate time series with an application to the US equity market
Stefanos Bennett, Mihai Cucuringu, Gesine Reinert
In multivariate time series systems, it has been observed that certain groups of variables partially lead the evolution of the system, while other variables follow this evolution w…