Global Explainability of GNNs via Logic Combination of Learned Concepts

1 code implementation • 13 Oct 2022 • Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Liò, Andrea Passerini

While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging.

Explaining the Explainers in Graph Neural Networks: a Comparative Study

2 code implementations • 27 Oct 2022 • Antonio Longa, Steve Azzolin, Gabriele Santin, Giulia Cencetti, Pietro Liò, Bruno Lepri, Andrea Passerini

Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process.

Node Classification

Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives

1 code implementation • WASSA (ACL) 2022 • Seyed Mahed Mousavi, Gabriel Roccabruna, Aniruddha Tammewar, Steve Azzolin, Giuseppe Riccardi

Deep Neural Networks (DNN) models have achieved acceptable performance in sentiment prediction of written text.

Multi-source Multi-domain Sentiment Analysis with BERT-based Models

1 code implementation • LREC 2022 • Gabriel Roccabruna, Steve Azzolin, Giuseppe Riccardi

Sentiment analysis is one of the most widely studied tasks in natural language processing.

Sentiment Analysis