ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions

22 April 2019

Papers citing "ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions"

9 / 9 papers shown

DINE: Dimensional Interpretability of Node EmbeddingsIEEE Transactions on Knowledge and Data Engineering (TKDE), 2023

245

02 Oct 2023

KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge DistillationLOG IN (LOG IN), 2023

V. Baltatzis

Luca Costabello

214

02 Oct 2023

Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples

Adrianna Janik

Luca Costabello

144

05 Dec 2022

Privacy and Transparency in Graph Machine Learning: A Unified Perspective

Megha Khosla

295

22 Jul 2022

Adversarial Robustness of Probabilistic Network Embedding for Link Prediction

153

05 Jul 2021

Learnt Sparsification for Interpretable Graph Neural Networks

191

23 Jun 2021

Zorro: Valid, Sparse, and Stable Explanations in Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering (TKDE), 2021

263

18 May 2021

CF-GNNExplainer: Counterfactual Explanations for Graph Neural NetworksInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021

Ana Lucic

Maartje ter Hoeve

Gabriele Tolomei

Maarten de Rijke

Fabrizio Silvestri

572

174

05 Feb 2021

GNNExplainer: Generating Explanations for Graph Neural Networks

622

1,656

10 Mar 2019