21 citations · 91 across the 17 of their papers we have counts for
17 papers
Spectral clustering of network time series via the sample covariance matrix
Brendan Martin, Joshua Agterberg, Mihai Cucuringu +2
Spectral clustering for community detection is analysed in multivariate time series models whose dependence structure is determined by an unobserved stochastic blockmodel. We estab…
Convergence guarantees for response prediction for latent structure network time series
Aranyak Acharyya, Francesco Sanna Passino, Michael W. Trosset +1
In this article, we propose a technique to predict the response associated with an unlabeled time series of networks in a semisupervised setting. Our model involves a collection of…
Doubly unfolded adjacency spectral embedding of dynamic multiplex graphs
Maximilian Baum, Francesco Sanna Passino, Axel Gandy
Many real-world networks evolve dynamically over time and present different types of connections between nodes, often called layers. In this work, we propose a latent position mode…
Targeted synthetic data generation for tabular data via hardness characterization
Tommaso Ferracci, Leonie Tabea Goldmann, Anton Hinel +1
Data augmentation via synthetic data generation has been shown to be effective in improving model performance and robustness in the context of scarce or low-quality data. Using the…
Approximate learning of parsimonious Bayesian context trees
Daniyar Ghani, Nicholas A. Heard, Francesco Sanna Passino
Models for categorical sequences typically assume exchangeable or first-order dependent sequence elements. These are common assumptions, for example, in models of computer malware…
NIRVAR: Network Informed Restricted Vector Autoregression
Brendan Martin, Francesco Sanna Passino, Mihai Cucuringu +1
High-dimensional panels of time series often arise in finance and macroeconomics, where co-movements within groups of panel components occur. Extracting these groupings from the da…
FinDKG: Dynamic Knowledge Graphs with Large Language Models for Detecting Global Trends in Financial Markets
Xiaohui Victor Li, Francesco Sanna Passino
Dynamic knowledge graphs (DKGs) are popular structures to express different types of connections between objects over time. They can also serve as an efficient mathematical tool to…
Online Bayesian changepoint detection for network Poisson processes with community structure
Joshua Corneck, Edward A. K. Cohen, James S. Martin +1
Network point processes often exhibit latent structure that govern the behaviour of the sub-processes. It is not always reasonable to assume that this latent structure is static, a…
Graph-based mutually exciting point processes for modelling event times in docked bike-sharing systems
Francesco Sanna Passino, Yining Che, Carlos Cardoso Correia Perello
This paper introduces graph-based mutually exciting processes (GB-MEP) to model event times in network point processes, focusing on an application to docked bike-sharing systems. G…
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks
Marcus A. K. September, Francesco Sanna Passino, Leonie Goldmann +1
Data preprocessing is a crucial part of any machine learning pipeline, and it can have a significant impact on both performance and training efficiency. This is especially evident…
Nested Dirichlet models for unsupervised attack pattern detection in honeypot data
Francesco Sanna Passino, Anastasia Mantziou, Daniyar Ghani +3
Cyber-systems are under near-constant threat from intrusion attempts. Attacks types vary, but each attempt typically has a specific underlying intent, and the perpetrators are typi…
Latent structure blockmodels for Bayesian spectral graph clustering
Francesco Sanna Passino, Nicholas A. Heard
Spectral embedding of network adjacency matrices often produces node representations living approximately around low-dimensional submanifold structures. In particular, hidden subst…
Mutually exciting point process graphs for modelling dynamic networks
Francesco Sanna Passino, Nicholas A. Heard
A new class of models for dynamic networks is proposed, called mutually exciting point process graphs (MEG). MEG is a scalable network-wide statistical model for point processes wi…
Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel
Francesco Sanna Passino, Nicholas A. Heard, Patrick Rubin-Delanchy
Spectral clustering is a popular method for community detection in network graphs: starting from a matrix representation of the graph, the nodes are clustered on a low dimensional…
Graph link prediction in computer networks using Poisson matrix factorisation
Francesco Sanna Passino, Melissa J. M. Turcotte, Nicholas A. Heard
Graph link prediction is an important task in cyber-security: relationships between entities within a computer network, such as users interacting with computers, or system librarie…
Link prediction in dynamic networks using random dot product graphs
Francesco Sanna Passino, Anna S. Bertiger, Joshua C. Neil +1
The problem of predicting links in large networks is an important task in a variety of practical applications, including social sciences, biology and computer security. In this pap…
Bayesian estimation of the latent dimension and communities in stochastic blockmodels
Francesco Sanna Passino, Nicholas A. Heard
Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theore…