Graph ML and clustering
Work on graph clustering, constrained graph partitioning, and structurally informed learning problems.
AI/MLGraph MLClustering
Publications
Papers across graph learning, scientific ML, and mathematical foundations.
A searchable record across graph ML, graph signal processing, spectral graph theory, and applied network analytics.
15
Peer-reviewed papers
150+
Citations
4
Research strands
2016-2025
Publication range
Research strands
A short map of the main publication themes.
The publication record spans graph ML and clustering, graph signal processing and scientific ML, spectral graph theory foundations, and applied network analytics.
Graph signal processing and scientific ML
Entropy and graph-signal methods for EEG, fMRI, DTI networks, and noisy multivariate data.
Graph Signal ProcessingScientific MLHealthcare
Spectral graph theory foundations
Mathematical work on magnetic Laplacians, spectral ordering, and the graph foundations behind later applied methods.
TheorySpectral methods
Applied network analytics
Applied modelling work that connects graph ideas with public-sector analysis and decision support.
ApplicationsPublic sector
Featured papers
Selected publications.
A few papers that anchor the main research themes before the full searchable list.
International Conference on Machine Learning (ICML) • 2025
Signed Laplacians for Constrained Graph Clustering
A graph clustering method that respects constraints while remaining mathematically principled and scalable enough to matter for real structured datasets.
AI/MLGraph MLClusteringTheory
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2024
Graph-based Permutation Patterns for the Analysis of Task-related fMRI Signals on DTI Networks in Mild Cognitive Impairment
A way to study how brain activity evolves across anatomical networks rather than treating signals as isolated time series.
HealthcareGraph Signal ProcessingApplicationsScientific ML
Chaos, Solitons and Fractals • 2023
Dispersion Entropy for Graph Signals
An entropy measure designed for graph-shaped data, making it easier to quantify complexity in signals that depend on network structure.
Graph Signal ProcessingApplicationsScientific ML
European Signal Processing Conference (EUSIPCO) • 2023
Graph-based Multivariate Multiscale Permutation Entropy: Study of Robustness to Noise and Application to Two-Phase Flow Data
Shows how graph-based complexity measures hold up when data are noisy and messy, not just mathematically clean.
Graph Signal ProcessingApplicationsScientific ML
Full record
Browse the complete publication list.
Search the full record by topic, application area, or method. Includes peer-reviewed papers, conference papers, and selected preprints.
Showing 18 records.
FeaturedAI/MLGraph MLClusteringTheory
Signed Laplacians for Constrained Graph Clustering
International Conference on Machine Learning (ICML) • 2025
An ICML paper on graph clustering with structural constraints, positioning this research directly within modern graph-based machine learning.
Details and citation
A graph clustering method that respects constraints while remaining mathematically principled and scalable enough to matter for real structured datasets.
J. S. Fabila-Carrasco and H. Sun. Signed Laplacians for Constrained Graph Clustering. Proceedings of the Forty-second International Conference on Machine Learning, 2025.
TheorySpectral methodsGraph learning
Isospectral Graphs via Spectral Bracketing
Linear Algebra and its Applications • 2024
A theoretical paper on spectral graph structure that strengthens the mathematical foundations behind later graph-analysis work.
Details and citation
J. S. Fabila-Carrasco, F. Lledo, and O. Post. Isospectral graphs via spectral bracketing. Linear Algebra and its Applications, 2024.
FeaturedHealthcareGraph Signal ProcessingApplicationsScientific ML
Graph-based Permutation Patterns for the Analysis of Task-related fMRI Signals on DTI Networks in Mild Cognitive Impairment
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2024
Applies graph-based signal analysis to neuroimaging data in mild cognitive impairment, showing method design on clinically difficult datasets.
Details and citation
A way to study how brain activity evolves across anatomical networks rather than treating signals as isolated time series.
J. S. Fabila-Carrasco, A. Campbell-Cousins, M. A. Parra-Rodriguez, and J. Escudero. Graph-based permutation patterns for the analysis of task-related fMRI signals on DTI networks in mild cognitive impairment. ICASSP, 2024.
FeaturedGraph Signal ProcessingApplicationsScientific ML
Dispersion Entropy for Graph Signals
Chaos, Solitons and Fractals • 2023
Introduces a graph-based entropy method for structured signals, useful when data live on networks rather than standard grids.
Details and citation
An entropy measure designed for graph-shaped data, making it easier to quantify complexity in signals that depend on network structure.
J. S. Fabila-Carrasco, C. Tan, and J. Escudero. Dispersion Entropy for Graph Signals. Chaos, Solitons and Fractals, 175:113977, 2023.
TheorySpectral methods
A Geometric Construction of Isospectral Magnetic Graphs
Analysis and Mathematical Physics • 2023
Extends foundational work on spectral graph structure and magnetic Laplacians.
Details and citation
J. S. Fabila-Carrasco, F. Lledo, and O. Post. A geometric construction of isospectral magnetic graphs. Analysis and Mathematical Physics, 13(64), 2023.
FeaturedGraph Signal ProcessingApplicationsScientific ML
Graph-based Multivariate Multiscale Permutation Entropy: Study of Robustness to Noise and Application to Two-Phase Flow Data
European Signal Processing Conference (EUSIPCO) • 2023
Tests graph-based multiscale entropy methods under noise and applies them to real-world flow data, emphasising robustness rather than idealised settings.
Details and citation
Shows how graph-based complexity measures hold up when data are noisy and messy, not just mathematically clean.
J. S. Fabila-Carrasco, C. Tan, and J. Escudero. Graph-based Multivariate Multiscale Permutation Entropy: Study of Robustness to Noise and Application to Two-Phase Flow Data. EUSIPCO, 2023.
FeaturedGraph Signal ProcessingApplicationsTheory
Permutation Entropy for Graph Signals
IEEE Transactions on Signal and Information Processing over Networks • 2022
A core paper developing entropy-style analysis for graph signals, bridging mathematics, signal processing, and data-driven applications.
Details and citation
Reworks permutation entropy so it can describe structured signals on networks instead of only ordinary time series.
J. S. Fabila-Carrasco, C. Tan, and J. Escudero. Permutation Entropy for Graph Signals. IEEE Transactions on Signal and Information Processing over Networks, 8:288-300, 2022.
TheorySpectral methods
Matching Number, Hamiltonian Graphs, and Magnetic Laplacian Matrices
Linear Algebra and its Applications • 2022
Foundational graph-theory work on magnetic Laplacians and structural graph properties.
Details and citation
J. S. Fabila-Carrasco, F. Lledo, and O. Post. Matching number, Hamiltonian graphs, and magnetic Laplacian matrices. Linear Algebra and its Applications, 642:86-100, 2022.
FeaturedGraph Signal ProcessingApplicationsScientific ML
Multivariate Permutation Entropy, a Cartesian Graph Product Approach
European Signal Processing Conference (EUSIPCO) • 2022
Extends entropy analysis to multivariate graph settings, supporting structured-signal analysis beyond standard time-series tools.
Details and citation
Treats multivariate signals as graph objects so relationships across channels can be analysed more explicitly.
J. S. Fabila-Carrasco, C. Tan, and J. Escudero. Multivariate permutation entropy, a Cartesian graph product approach. EUSIPCO, 2022.
TheorySpectral methodsGraph learning
Spectral Preorder and Perturbations of Discrete Weighted Graphs
Mathematische Annalen • 2020
A high-level mathematics paper on comparing weighted graphs through spectral structure.
Details and citation
J. S. Fabila-Carrasco, F. Lledo, and O. Post. Spectral preorder and perturbations of discrete weighted graphs. Mathematische Annalen, 382:1775-1823, 2020.
TheoryApplicationsSpectral methods
Covering Graphs, Magnetic Spectral Gaps and Applications to Polymers and Nanoribbons
Symmetry • 2019
Explores magnetic spectral gaps with applications to physical graph-based systems.
Details and citation
J. S. Fabila-Carrasco and F. Lledo. Covering graphs, magnetic spectral gaps and applications to polymers and nanoribbons. Symmetry, 11(9):1163, 2019.
TheorySpectral methods
Spectral Gaps and Discrete Magnetic Laplacians
Linear Algebra and its Applications • 2018
An early foundational paper on discrete magnetic Laplacians and spectral graph analysis.
Details and citation
J. S. Fabila-Carrasco, F. Lledo, and O. Post. Spectral gaps and discrete magnetic Laplacians. Linear Algebra and its Applications, 547:183-216, 2018.
ApplicationsPublic sectorStatistical modelling
Trends in Traffic Fatalities in Mexico: Examining Progress on the Decade of Action for Road Safety 2011-2020
International Journal of Public Health • 2016
An applied public-health paper tied to large-scale national data and policy-facing analysis in Mexico.
Details and citation
A. Cervantes-Trejo, I. Leenen, J. S. Fabila-Carrasco, and R. Rojas-Vargas. Trends in traffic fatalities in Mexico: examining progress on the decade of action for road safety 2011-2020. International Journal of Public Health, 61:903-913, 2016.
Graph Signal ProcessingApplicationsScientific ML
Multivariate Permutation Entropy via the Cartesian Graph Product to Analyse Two-Phase Flow
Complex Networks and Their Applications • 2022
Conference work applying graph-based entropy methods to complex flow data.
Details and citation
J. S. Fabila-Carrasco, C. Tan, and J. Escudero. Multivariate permutation entropy via the Cartesian graph product to analyse two-phase flow. Complex Networks and Their Applications, 2022.
Graph Signal ProcessingApplicationsScientific ML
Entropy Metrics for Graph Signals
Complex Networks and Their Applications • 2021
Conference paper establishing entropy-oriented analysis for graph signals and networked data.
Details and citation
J. S. Fabila-Carrasco, C. Tan, and J. Escudero. Entropy metrics for graph signals. Complex Networks and Their Applications, 2021.
AI/MLGraph Signal ProcessingPreprint
Graph Permutation Entropy: Extensions to the Continuous Case, A Step Towards Ordinal Deep Learning, and More
arXiv • 2024
A preprint pushing graph permutation entropy toward continuous settings and more ML-adjacent directions.
Details and citation
O. Roy, A. Campbell-Cousins, J. S. Fabila-Carrasco, M. Parra, and J. Escudero. Graph Permutation Entropy: Extensions to the Continuous Case, A step towards Ordinal Deep Learning, and More. arXiv, 2024.
Graph Signal ProcessingApplicationsPreprint
Graph-Based Multivariate Multiscale Dispersion Entropy: Efficient Implementation and Applications to Real-World Network Data
arXiv • 2024
A preprint focused on efficient implementation and real-world network data applications.
Details and citation
J. S. Fabila-Carrasco, C. Tan, and J. Escudero. Graph-Based Multivariate Multiscale Dispersion Entropy: Efficient Implementation and Applications to Real-World Network Data. arXiv, 2024.
ApplicationsScientific MLPreprint
Dispersion Transition Network and the Quantification of Transition Information in Time Series
Preprint • 2025
A methods-oriented preprint on transition information in time series and structured dynamics.
Details and citation
B. Zhang, J. S. Fabila-Carrasco, D. Garcia Cava, and J. Escudero. Dispersion transition network and the quantification of transition information in time series. Preprint, 2025.