42 citations · 83 across the 10 of their papers we have counts for
12 papers
Robust Stochastic Graph Generator for Counterfactual Explanations
Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo
Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI systems. While extensively researched in domains suc…
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges
Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo +1
Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide counter-examples to overcome the transparency…
Modeling Quality and Machine Learning Pipelines through Extended Feature Models
Giordano d'Aloisio, Antinisca Di Marco, Giovanni Stilo
The recently increased complexity of Machine Learning (ML) methods, led to the necessity to lighten both the research and industry development processes. ML pipelines have become a…
GRETEL: A unified framework for Graph Counterfactual Explanation Evaluation
Mario Alfonso Prado-Romero, Giovanni Stilo
Machine Learning (ML) systems are a building part of the modern tools which impact our daily life in several application domains. Due to their black-box nature, those systems are h…
Network connectivity under a probabilistic node failure model
Lucia Cavallaro, Stefania Costantini, Pasquale De Meo +2
Centrality metrics have been widely applied to identify the nodes in a graph whose removal is effective in decomposing the graph into smaller sub-components. The node--removal proc…
ECIR 2020 Workshops: Assessing the Impact of Going Online
Sérgio Nunes, Suzanne Little, Sumit Bhatia +11
ECIR 2020 https://ecir2020.org/ was one of the many conferences affected by the COVID-19 pandemic. The Conference Chairs decided to keep the initially planned dates (April 14-17, 2…