Now showing items 1-12 of 12

    • Bayesian inference of spatial and temporal relations in AI patents for EU countries 

      Rusek, Krzysztof; Kleszcz, Agnieszka; Cabellos Aparicio, Alberto (2023-04-29)
      Article
      Open Access
      In the paper, we propose two models of Artificial Intelligence (AI) patents in European Union (EU) countries addressing spatial and temporal behaviour. In particular, the models can quantitatively describe the interaction ...
    • Challenging the generalization capabilities of Graph Neural Networks for network modeling 

      Suárez-Varela Maciá, José Rafael; Carol Bosch, Sergi; Rusek, Krzysztof; Almasan Puscas, Felician Paul; Arias Vicente, Marta; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Association for Computing Machinery (ACM), 2019)
      Conference report
      Open Access
      Today, network operators still lack functional network models able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay or jitter) at limited cost. Recently a novel Graph Neural Network (GNN) ...
    • Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case 

      Almasan Puscas, Felician Paul; Suárez-Varela Maciá, José Rafael; Rusek, Krzysztof; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Elsevier, 2022-12-01)
      Article
      Restricted access - publisher's policy
      Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated control problems. Consequently, DRL represents a promising technique to efficiently solve many relevant optimization ...
    • Fast traffic engineering by gradient descent with learned differentiable routing 

      Rusek, Krzysztof; Almasan Puscas, Felician Paul; Suárez-Varela Maciá, José Rafael; Cholda, Piotr; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Institute of Electrical and Electronics Engineers (IEEE), 2022)
      Conference lecture
      Open Access
      Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewise, the ever-faster traffic dynamics will ...
    • Graph neural networks for communication networks: context, use cases and opportunities 

      Suárez-Varela Maciá, José Rafael; Almasan Puscas, Felician Paul; Ferriol Galmés, Miquel; Rusek, Krzysztof; Geyer, Fabien; Cheng, Xiangle; Shi, Xiang; Xiao, Shihan; Scarselli, Franco; Cabellos Aparicio, Alberto; Barlet Ros, Pere (2023-05)
      Article
      Open Access
      Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise ...
    • RiskNet: neural risk assessment in networks of unreliable resources 

      Rusek, Krzysztof; Borylo, Piotr; Jaglarz, Piotr; Geyer, Fabien; Cabellos Aparicio, Alberto; Cholda, Piotr (Springer Nature, 2023-07-15)
      Article
      Open Access
      We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup ...
    • RouteNet-Erlang: A graph neural network for network performance evaluation 

      Ferriol Galmés, Miquel; Rusek, Krzysztof; Suárez-Varela Maciá, José Rafael; Xiao, Shihan; Shi, Xiang; Cheng, Xiangle; Wu, Bo; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Institute of Electrical and Electronics Engineers (IEEE), 2022)
      Conference report
      Open Access
      Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the ...
    • RouteNet-Fermi: network modeling with graph neural networks 

      Ferriol Galmés, Miquel; Paillissé Vilanova, Jordi; Suárez-Varela Maciá, José Rafael; Rusek, Krzysztof; Xiao, Shihan; Shi, Xiang; Cheng, Xiangle; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Institute of Electrical and Electronics Engineers (IEEE), 2023-12)
      Article
      Open Access
      Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complexity, some models present limitations, such ...
    • RouteNet: leveraging graph neural networks for network modeling and optimization in SDN 

      Rusek, Krzysztof; Suárez-Varela Maciá, José Rafael; Almasan Puscas, Felician Paul; Barlet Ros, Pere; Cabellos Aparicio, Alberto (2020-06-05)
      Article
      Open Access
      Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key ...
    • The graph neural networking challenge: a worldwide competition for education in AI/ML for networks 

      Suárez-Varela Maciá, José Rafael; Ferriol Galmés, Miquel; López Brescó, Albert; Almasan Puscas, Felician Paul; Bernárdez Gil, Guillermo; Pujol Perich, David; Rusek, Krzysztof; Bonniot, Loïck; Neumann, Christoph; Schnitzler, François; Taïani, François; Happ, Martin; Barlet Ros, Pere; Cabellos Aparicio, Alberto (2021-07-01)
      Article
      Open Access
      During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in ...
    • Towards more realistic network models based on Graph Neural Networks 

      Badia Sampera, Arnau; Suárez-Varela Maciá, José Rafael; Almasan Puscas, Felician Paul; Rusek, Krzysztof; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Association for Computing Machinery (ACM), 2019)
      Conference report
      Open Access
      Recently, a Graph Neural Network (GNN) model called RouteNet was proposed as an efficient method to estimate end-to-end network performance metrics such as delay or jitter, given the topology, routing, and traffic of the ...
    • Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN 

      Rusek, Krzysztof; Suárez-Varela Maciá, José Rafael; Mestres Sugrañes, Albert; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Association for Computing Machinery (ACM), 2019)
      Conference report
      Open Access
      Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques ...