no code implementations • 10 Aug 2021 • George Panagopoulos, Nikolaos Tziortziotis, Michalis Vazirgiannis, Fragkiskos D. Malliaros
Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem.
4 code implementations • 15 Apr 2021 • Benedek Rozemberczki, Paul Scherer, Yixuan He, George Panagopoulos, Alexander Riedel, Maria Astefanoaei, Oliver Kiss, Ferenc Beres, Guzmán López, Nicolas Collignon, Rik Sarkar
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing.
no code implementations • 1 Jan 2021 • Giannis Nikolentzos, George Panagopoulos, Michalis Vazirgiannis
Graph neural networks and graph kernels have achieved great success in solving machine learning problems on graphs.
5 code implementations • 10 Sep 2020 • George Panagopoulos, Giannis Nikolentzos, Michalis Vazirgiannis
Furthermore, to account for the limited amount of training data, we capitalize on the pandemic's asynchronous outbreaks across countries and use a model-agnostic meta-learning based method to transfer knowledge from one country's model to another's.
no code implementations • 11 Jun 2020 • Paul Boniol, George Panagopoulos, Christos Xypolopoulos, Rajaa El Hamdani, David Restrepo Amariles, Michalis Vazirgiannis
Artificial Intelligence techniques are already popular and important in the legal domain.
3 code implementations • 18 Apr 2019 • George Panagopoulos, Fragkiskos D. Malliaros, Michalis Vazirgiannis
The first part of our methodology is a multi-task neural network that learns embeddings of nodes that initiate cascades (influencer vectors) and embeddings of nodes that participate in them (susceptible vectors).
1 code implementation • 20 Jun 2018 • George Panagopoulos
In this work, we perform a review of methods for neural circuit inference given the activation time series of the neural population.