George Panagopoulos

C. Kosma, G. Nikolentzos, G. Panagopoulos, J.M. Steyaert, M. Vazirgiannis

Neural Ordinary Differential Equations for Modeling Epidemic Spreading

Transactions on Machine Learning Research,2023

Short Description: A graph neural ordinary differential equation model to predict the Susceptible-Infectious-Recovered epidemic model. [pdf] [code]

G. Panagopoulos, F. Malliaros and M. Vazirgiannis

Multi-task Learning for Influence Estimation and Maximization

IEEE Transactions On Knowledge and Data Engineering (2020) Impact Factor: 4.935

Short Description: A multi-task influence learnng model and an algorithm that uses the learnt representations to perform efficient influence maximization. [code] [video]

G. Panagopoulos and I. Pavlidis

Forecasting Markers of Habitual Driving Behaviors Associated With Crash Risk

IEEE Transactions On Intelligent Transportation Systems (2019) Impact Factor: 4.051

Short Description: An extreme gradient boosting model that takes as input the physiological signals of a driver and vehicle indications and provides short-term predictions of distracted or aggressive driving. [code] [presentation]

I. Pavlidis, D. Zavlin, A. Khatri, A. Wesley, G. Panagopoulos and A. Echo

Absence of Stressful Conditions Accelerates Dexterous Skill Acquisition in Surgery

Nature Scientific Reports (2019) Impact Factor: 4.122

Short Description: Analysis of an experiment that dealt with the skill acquisition of surgigal trainees, using statistical inference and visualization of physiological recordings and questionairs. [data]

G. Panagopoulos, G. Tsatsaronis and I. Varlamis

Detecting Rising Stars in Dynamic Collaborative Networks

Elsevier Journal of Informetrics (2017) Impact Factor: 3.879

Short Description: Identify young scientists with high potential, using social network analysis and unsupervised machine learning. [code]

G. Panagopoulos, D. Malitesta, F. D Malliaros, J. Pang

Uplift Modeling Under Limited Supervision

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), 2024

Short Description: A framework based on GNNs and active learning for causal effect prediction on bipartite networks, indicating their potential to diminish the required demand for training labels [pdf] [code]

G. Panagopoulos, N. Tziortziotis, F. Malliaros, M. Vazirgiannis

Maximizing Influence with Graph Neural Networks

International Conference on Social Networks Analysis and Mining (ASONAM),2023

Short Description: A graph neural network to predict the Independent Cascade model and a submodular algorithm based on its representations to perform influence maximization. [pdf] [code]

B. Rozemberczki, P. Scherer, Y. He, G. Panagopoulos, M. Astefanoaei, O. Kiss, F. Beres, N. Collignon, R. Sarkar

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models Best Resource Paper

Conference on Information and Knowledge Management (CIKM resource track),2021

Short Description: A library with temporal graph neural network methods and temporal graph datasets. [pdf] [code]

G Nikolentzos, G Panagopoulos, M Vazirgiannis.

An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks

International Conference on Artificial Neural Networks (ICANN),2021

Short Description: An experimental study to quantify the accuracy of graph kernels and graph neural networks for approximating graph similarity using a well-known similarity function for evaluation. [pdf]

G. Panagopoulos, G. Nikoletzos, M. Vazirgiannis.

Transfer Graph Neural Networks for Pandemic Forecasting

AAAI International Conference on Artificial Intelligence (AAAI),2021

Short Description: A graph neural network that uses temporal mobility networks and the recent history of the epidemic to predict the number of COVID-19 cases per day in NUTS3 geographical regions of different EU countries. [pdf] [code]

G. Panagopoulos, F. Malliaros, M. Vazirgiannis.

Influence Maximization using Influence and Susceptibility Embeddings Best paper nomination

AAAI International Conference on Web and Social Media (ICWSM), 2020

Short Description: An influence maximization method based on influence representation learning from diffusion cascades. [pdf] [code] [video]

G. Panagopoulos, C. Xypolopoulos, K. Skianis, C. Giatsidis, J. Tang, M. Vazirgiannis.

Scientometrics for Success and Influence in the Microsoft Academic Graph

International Conference on Complex Networks and Their Applications (Complex Networks), 2019

Short Description: An online application with visualizations on scientific success, using field-based h-index and large scale d-core decomposition on the MAG. [code] [app] [presentation]

G. Panagopoulos, F. Malliaros, M. Vazirgiannis

DiffuGreedy: An Influence Maximization Algorithm based on Diffusion Cascades

International Conference on Complex Networks and Their Applications (Complex Networks), 2018

Short Description: We propose a new algorithm to perform influence maximization utilizing diffusion cascades that have taken place over the network. [pdf][code]

G. Panagopoulos

Multi-Task Learning for Commercial Brain Computer Interfaces

IEEE BioInformatics and BioEngineering (BIBE), 2017

Short Description: Apply and compare conventional and multi-task machine learning algorithms used in Brain Computer Interface literature in two open datasets of mental monitoring experiments which utilized Neurosky EEG device. [pdf] [code] [presentation] [poster]

P. Karampiperis, A. Koukourikos G. Panagopoulos

From Computational Creativity Metrics to the Principal Components of Human Creativity

Knowledge, Information and Creativity Support Systems (KICSS), 2014

Short Description: Model the creativity of children playing serious games in school, with natural language processing and semantic analysis. [pdf] [code]

A Koukourikos, P Karampiperis, G. Panagopoulos

Creative Stories: A Storytelling Game fostering Creativity

Cognition and Exploratory Learning in Digital Age (CELDA), 2014

Short Description: A serious game using computational semantic lateral thinking techniques to motivate children write more creative essays. [pdf] [code] [presentation]

R. Bresson, G. Nikolentzos, G. Panagopoulos, M. Chatzianastasis, J. Pang, M. Vazirgiannis

KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning

2024

Short Description: An extenssive experimental study to quantify the effect of substituting MLPs with KANs for several graph learning tasks [pdf] [code]

G. Panagopoulos, H. Jalalzai

Graph Neural Networks with Extreme Nodes Discrimination

Deep Learning on Graphs: Methods and Applications, KDD 2020

Short Description: An experimental analysis on the use of extreme value theory in semi-supervised classification using graph neural networks. [pdf] [code]

P. Boniol, G. Panagopoulos, C. Xypolopoulos, R. El Hamdani, D. Restrepo Amariles, M. Vazirgiannis

Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France

Workshop on Natural Legal Language Processing, KDD 2020

Short Description: Text mining and analysis of entities in legal documents of French appeal court decisions. [pdf]

G. Panagopoulos, C. Palmer

A Specialized Interactive Data Application for EEG Based Sleep Studies

Workshop on Assistive Technologies for Decision making in Healthcare, PETRA 2017

Short Description: A web data application using signal processing techniques and visualizations to assist sleep EEG analysis in a study with anxious children. [pdf] [code] [presentation]

G. Panagopoulos, P. Karampiperis, A. Koukourikos, S. Konstantinidis

Creativity Profiling Server: Modelling the Principal Components of Human Creativity over Texts

Workshop on Deep Content Analytics Techniques for Personalized and Intelligent Services, UMAP 2015

Short Description: A server modeling a user's creativity based on textual exhibits, employing computational creativity metrics and unsupervised learning. [pdf] [code] [presentation]

G. Panagopoulos

Network Inference from Neural Activation Time Series: A comparative review

International Conference on Network Science, 2018

Short Description: Apply and compare different network inference techniques to Kaggle connectomics small dataset. [pdf] [code] [poster]