Fetching the paper…
Reading the bibliography…
This paper presents Gem, a model-agnostic approach for providing interpretable explanations for any GNNs on various graph learning tasks.
The Theory of Prediction. Modern Mathematics for Engineers
Wiener, N · 1956
Earlier work this paper cites.
A Reduction of a Graph to a Canonical Form and an Algebra Arising during this Reduction
Weisfeiler, B. and Lehman, A. A · 1968
Earlier work this paper cites.
Investigating Causal Relations by Econometric Models and Cross-Spectral Methods
Granger, C. W · 1969
Earlier work this paper cites.
Testing for Causality: a Personal Viewpoint
Granger, C. W · 1980
Earlier work this paper cites.
Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning
Pearl, J · 1985
Earlier work this paper cites.
Structure-Activity Relationship of Mutagenic Aromatic and Heteroaromatic Nitro Compounds. Correlation with Molecular Orbital Energies and Hydrophobicity
Debnath, A. K., Lopez de Compadre, R. L., Debnath, G., Shusterman, A. J., and Hansch, C · 1991
Earlier work this paper cites.
Simultaneous assessment of flow and bold signals in resting-state functional connectivity maps
Biswal, B. B., Kylen, J. V., and Hyde, J. S · 1997
Earlier work this paper cites.
Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification
Wale, N., Watson, I. A., and Karypis, G · 2008
Earlier work this paper cites.
Causality
Pearl, J · 2009
Earlier work this paper cites.
Wiener–Granger Causality: a Well Established Methodology
Bressler, S. L. and Seth, A. K · 2011
Earlier work this paper cites.
Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation
Goldstein, A., Kapelner, A., Bleich, J., and Pitkin, E · 2015
Earlier work this paper cites.
Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems
Datta, A., Sen, S., and Zick, Y · 2016
Cited alongside, same era.
Variational Graph Auto-Encoders
Kipf, T. N. and Welling, M · 2016
Cited alongside, same era.
Model-Agnostic Interpretability of Machine Learning
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Cited alongside, same era.
Inductive Representation Learning on Large Graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N. and Welling, M · 2017
Cited alongside, same era.
A Unified Approach to Interpreting Model Predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
Modeling Polypharmacy Side Effects with Graph Convolutional Networks
Zitnik, M., Agrawal, M., and Leskovec, J · 2018
Later among the works it cites.
Neural Network Attributions: A Causal Perspective
Chattopadhyay, A., Manupriya, P., Sarkar, A., and Balasubramanian, V. N · 2019
Later among the works it cites.
Explainability Methods for Graph Convolutional Neural networks
Pope, P. E., Kolouri, S., Rostami, M., Martin, C. E., and Hoffmann, H · 2019
Later among the works it cites.
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Schwab, P. and Karlen, W · 2019
Later among the works it cites.
GNNExplainer: Generating Explanations for Graph Neural Networks
Ying, Z., Bourgeois, D., You, J., Zitnik, M., and Leskovec, J · 2019
Later among the works it cites.
Towards Explainable Graph Representations in Digital Pathology
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Predicting Multicellular Function Through Multi-Layer Tissue Networks
Zitnik, M. and Leskovec, J · 2017
Cited alongside, same era.
Explaining Deep Learning Models using Causal Inference
Narendra, T., Sankaran, A., Vijaykeerthy, D., and Mani, S · 2018
Cited alongside, same era.
Graph Attention Networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Cited alongside, same era.
Link Prediction Based on Graph Neural Networks
Zhang, M. and Chen, Y · 2018
Cited alongside, same era.
Learning Deep Generative Models of Graphs
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., and Battaglia, P
Cited in the paper.
Multi-Objective De Novo Drug Design with Conditional Graph Generative Model
Li, Y., Zhang, L., and Liu, Z
Cited in the paper.
Jaume, G., Pati, P., Foncubierta-Rodriguez, A., Feroce, F., Scognamiglio, G., Anniciello, A. M., Thiran, J.-P., Goksel, O., and Gabrani, M · 2020
Later among the works it cites.
Guardian
Lin, W., Gao, Z., and Li, B · 2020
Later among the works it cites.
Parameterized Explainer for Graph Neural Network
Luo, D., Cheng, W., Xu, D., Yu, W., Zong, B., Chen, H., and Zhang, X · 2020
Later among the works it cites.
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
Vu, M. N. and Thai, M. T · 2020
Later among the works it cites.
XGNN: Towards Model-Level Explanations of Graph Neural Networks
Yuan, H., Tang, J., Hu, X., and Ji, S · 2020
Later among the works it cites.