activity
20082025
most citedMap Equation Centrality: Community-aware Centrality based on the Map Equation

26 citations · 52 across the 16 of their papers we have counts for

collaborators

18 papers

nucl-th2025

Contact potentials in presence of a regular finite-range interaction using dimensional regularization and the method

David R. Entem, Juan Nieves, Jose Antonio Oller

We solve the Lippman-Schwinger equation (LSE) with a kernel that includes a regular finite-range potential and additional contact terms with derivatives. We employ distorted wave t…

cs.AI2024

Disagree and Commit: Degrees of Argumentation-based Agreements

Timotheus Kampik, Juan Carlos Nieves

In cooperative human decision-making, agreements are often not total; a partial degree of agreement is sufficient to commit to a decision and move on, as long as one is somewhat co…

cs.CY2023

ACROCPoLis: A Descriptive Framework for Making Sense of Fairness

Andrea Aler Tubella, Dimitri Coelho Mollo, Adam Dahlgren Lindström +8

Fairness is central to the ethical and responsible development and use of AI systems, with a large number of frameworks and formal notions of algorithmic fairness being available.…

cs.CY2022★ 2 cited

Good AI for Good: How AI Strategies of the Nordic Countries Address the Sustainable Development Goals

Andreas Theodorou, Juan Carlos Nieves, Virginia Dignum

Developed and used responsibly Artificial Intelligence (AI) is a force for global sustainable development. Given this opportunity, we expect that the many of the existing guideline…

cs.SI2022★ 26 cited

Map Equation Centrality: Community-aware Centrality based on the Map Equation

Christopher Blöcker, Juan Carlos Nieves, Martin Rosvall

To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network str…

cs.AI2021

Interrogating the Black Box: Transparency through Information-Seeking Dialogues

Andrea Aler Tubella, Andreas Theodorou, Juan Carlos Nieves

This paper is preoccupied with the following question: given a (possibly opaque) learning system, how can we understand whether its behaviour adheres to governance constraints? The…