Global Explainability of GNNs via Logic Combination of Learned Concepts

13 October 2022

Papers citing "Global Explainability of GNNs via Logic Combination of Learned Concepts"

7 / 7 papers shown

Title
Logical Distillation of Graph Neural Networks Alexander Pluska Pascal Welke Thomas Gärtner Sagar Malhotra 26 0 0 11 Jun 2024
MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation Zhaoning Yu Hongyang Gao 37 3 0 21 May 2024
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning F. Mumuni A. Mumuni AAML 25 5 0 11 Mar 2024
Explaining the Explainers in Graph Neural Networks: a Comparative Study Antonio Longa Steve Azzolin G. Santin G. Cencetti Pietro Lio' Bruno Lepri Andrea Passerini 42 27 0 27 Oct 2022
Algorithmic Concept-based Explainable Reasoning Dobrik Georgiev Pietro Barbiero Dmitry Kazhdan Petar Velivcković Pietro Lió 61 16 0 15 Jul 2021
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 162 589 0 31 Dec 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 120 293 0 17 Oct 2019