Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification

Xiaoxiao Li, João Saúde

Type: Preprint

Publication Date: 2020-01-01

Citations: 3

DOI: https://doi.org/10.48550/arxiv.2002.00514

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arXiv (Cornell University) - View - PDF
HAL (Le Centre pour la Communication Scientifique Directe) - View - PDF
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+	A family of statistical symmetric divergences based on Jensen's inequality	2010	Frank Nielsen
+	Semi-Supervised Classification with Graph Convolutional Networks	2016	Thomas Kipf Max Welling
+	Modeling Relational Data with Graph Convolutional Networks	2018	Michael Schlichtkrull Thomas Kipf Peter Bloem Rianne van den Berg Ivan Titov Max Welling
+	Inductive Representation Learning on Large Graphs	2017	William L. Hamilton Rex Ying Jure Leskovec
+	Edge Attention-based Multi-Relational Graph Convolutional Networks	2018	Chao Shang Qinqing Liu Ko‐Shin Chen Jiangwen Sun Jin Lü Jinfeng Yi Jinbo Bi
+	Relational inductive biases, deep learning, and graph networks	2018	Peter Battaglia Jessica B. Hamrick Victor Bapst Álvaro Sánchez‐González Vinícius Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner
+	Deep Learning on Graphs: A Survey	2018	Ziwei Zhang Peng Cui Wenwu Zhu
+	Graph Neural Networks: A Review of Methods and Applications	2018	Jie Zhou Ganqu Cui Shengding Hu Zhengyan Zhang Cheng Yang Zhiyuan Liu Lifeng Wang Changcheng Li Maosong Sun
+	GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks	2019	Rex Ying Dylan Bourgeois Jiaxuan You Marinka Žitnik Jure Leskovec
+	Explainability Techniques for Graph Convolutional Networks.	2019	Federico Baldassarre Hossein Azizpour