1 code implementation • 27 Mar 2024 • Gesine Reinert, Wenkai Xu
Generating graphs that preserve characteristic structures while promoting sample diversity can be challenging, especially when the number of graph observations is small.
no code implementations • 10 Feb 2024 • Gholamali Aminian, Yixuan He, Gesine Reinert, Łukasz Szpruch, Samuel N. Cohen
This work provides a theoretical framework for assessing the generalization error of graph classification tasks via graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points.
1 code implementation • 2 Feb 2024 • Ruikang Ouyang, Andrew Elliott, Stratis Limnios, Mihai Cucuringu, Gesine Reinert
For analysing real-world networks, graph representation learning is a popular tool.
1 code implementation • 9 Oct 2023 • Yixuan He, Gesine Reinert, David Wipf, Mihai Cucuringu
The angular synchronization problem aims to accurately estimate (up to a constant additive phase) a set of unknown angles $\theta_1, \dots, \theta_n\in[0, 2\pi)$ from $m$ noisy measurements of their offsets $\theta_i-\theta_j \;\mbox{mod} \; 2\pi.$ Applications include, for example, sensor network localization, phase retrieval, and distributed clock synchronization.
no code implementations • 29 Jun 2023 • Stratis Limnios, Praveen Selvaraj, Mihai Cucuringu, Carsten Maple, Gesine Reinert, Andrew Elliott
SaGess then constructs a synthetic graph using the subgraphs that have been generated by DiGress.
no code implementations • 18 Feb 2023 • Yutong Lu, Gesine Reinert, Mihai Cucuringu
The time proximity of trades across stocks reveals interesting topological structures of the equity market in the United States.
1 code implementation • 11 Oct 2022 • Moritz Weckbecker, Wenkai Xu, Gesine Reinert
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a powerful tool for the goodness of fit tests, especially in high dimensions; however, the test performance may depend on the choice of kernels in an underlying reproducing kernel Hilbert space (RKHS).
no code implementations • 21 Sep 2022 • Yutong Lu, Gesine Reinert, Mihai Cucuringu
The time proximity of high-frequency trades can contain a salient signal.
1 code implementation • 1 Sep 2022 • Yixuan He, Michael Permultter, Gesine Reinert, Mihai Cucuringu
In these experiments, we consider tasks related to signed information, tasks related to directional information, and tasks related to both signed and directional information.
1 code implementation • 31 May 2022 • Wenkai Xu, Gesine Reinert
Assessing the quality of such synthetic data generators hence has to be addressed.
1 code implementation • 28 Mar 2022 • Jase Clarkson, Mihai Cucuringu, Andrew Elliott, Gesine Reinert
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 dynamics are core objects of study.
no code implementations • 17 Mar 2022 • James Wilsenach, Katie Warnaby, Charlotte M. Deane, Gesine Reinert
Our analysis pipeline provides a way to characterise dynamic network activity of the brain under novel behaviours or conditions.
1 code implementation • 7 Mar 2022 • Wenkai Xu, Gesine Reinert
We propose and analyse a novel statistical procedure, coined AgraSSt, to assess the quality of graph generators that may not be available in explicit form.
1 code implementation • 22 Feb 2022 • Yixuan He, Xitong Zhang, JunJie Huang, Benedek Rozemberczki, Mihai Cucuringu, Gesine Reinert
While many networks are signed or directed, or both, there is a lack of unified software packages on graph neural networks (GNNs) specially designed for signed and directed networks.
1 code implementation • 1 Feb 2022 • Yixuan He, Quan Gan, David Wipf, Gesine Reinert, Junchi Yan, Mihai Cucuringu
In this paper, we introduce neural networks into the ranking recovery problem by proposing the so-called GNNRank, a trainable GNN-based framework with digraph embedding.
no code implementations • 20 Jan 2022 • Stefanos Bennett, Mihai Cucuringu, Gesine Reinert
In this paper, we propose a method for the detection of lead-lag clusters of time series in multivariate systems.
1 code implementation • 13 Oct 2021 • Yixuan He, Gesine Reinert, Songchao Wang, Mihai Cucuringu
Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited.
1 code implementation • 9 Jun 2021 • Yixuan He, Gesine Reinert, Mihai Cucuringu
DIGRAC optimizes directed flow imbalance for clustering without requiring label supervision, unlike existing graph neural network methods, and can naturally incorporate node features, unlike existing spectral methods.
1 code implementation • 28 Feb 2021 • Wenkai Xu, Gesine Reinert
We propose and analyse a novel nonparametric goodness of fit testing procedure for exchangeable exponential random graph models (ERGMs) when a single network realisation is observed.
1 code implementation • 2 Jan 2019 • Andrew Elliott, Mihai Cucuringu, Milton Martinez Luaces, Paul Reidy, Gesine Reinert
The first set of synthetic networks was split in a training set of 70 percent of the networks, and a test set of 30 percent of the networks.
Applications Social and Information Networks Physics and Society 05C82
1 code implementation • 2 Apr 2017 • Anatol E. Wegner, Luis Ospina-Forero, Robert E. Gaunt, Charlotte M. Deane, Gesine Reinert
Many complex systems can be represented as networks, and the problem of network comparison is becoming increasingly relevant.