Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
2006.03589
Cited By
v1
v2
v3 (latest)
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
5 June 2020
Thomas Schnake
Oliver Eberle
Jonas Lederer
Shinichi Nakajima
Kristof T. Schütt
Klaus-Robert Muller
G. Montavon
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Higher-Order Explanations of Graph Neural Networks via Relevant Walks"
50 / 120 papers shown
ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining
Bharat Sharman
Elkafi Hassini
216
0
0
28 Nov 2025
Graph Diffusion Counterfactual Explanation
David Bechtoldt
Sidney Bender
DiffM
120
3
0
20 Nov 2025
Order-Level Attention Similarity Across Language Models: A Latent Commonality
Jinglin Liang
Jin Zhong
Shuangping Huang
Yunqing Hu
Huiyuan Zhang
Huifang Li
Lixin Fan
Hanlin Gu
167
1
0
07 Nov 2025
Algorithmic Primitives and Compositional Geometry of Reasoning in Language Models
Samuel Lippl
Thomas McGee
Kimberly Lopez
Ziwen Pan
Pierce Zhang
Salma Ziadi
Oliver Eberle
Ida Momennejad
LRM
185
0
0
13 Oct 2025
GnnXemplar: Exemplars to Explanations -- Natural Language Rules for Global GNN Interpretability
Burouj Armgaan
Eshan Jain
Harsh Pandey
Mahesh Chandran
Jignesh M. Patel
LLMAG
398
2
0
22 Sep 2025
Database Views as Explanations for Relational Deep Learning
Agapi Rissaki
Ilias Fountalis
Wolfgang Gatterbauer
Benny Kimelfeld
260
0
0
11 Sep 2025
RelP: Faithful and Efficient Circuit Discovery in Language Models via Relevance Patching
F. Jafari
Oliver Eberle
Ashkan Khakzar
Neel Nanda
KELM
340
4
0
28 Aug 2025
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Lingkai Kong
Haotian Sun
Yuchen Zhuang
Haorui Wang
Wenhao Mu
Chao Zhang
BDL
209
5
0
23 Aug 2025
Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies
Fanzhen Liu
Xiaoxiao Ma
Jian Yang
A. Abuadbba
Kristen Moore
Surya Nepal
Cécile Paris
Quan Z. Sheng
Jia Wu
221
1
0
15 Aug 2025
Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
Florian Bley
Jacob R. Kauffmann
Simon León Krug
Klaus-Robert Müller
G. Montavon
FAtt
230
0
0
05 Aug 2025
Explaining GNN Explanations with Edge Gradients
Knowledge Discovery and Data Mining (KDD), 2025
Jesse He
Akbar Rafiey
Gal Mishne
Yusu Wang
AAML
FAtt
248
4
0
01 Aug 2025
Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework
Laura Kopf
Nils Feldhus
Kirill Bykov
P. Bommer
Anna Hedström
Marina M.-C. Höhne
Oliver Eberle
501
8
0
18 Jun 2025
Wasserstein Distances Made Explainable: Insights Into Dataset Shifts and Transport Phenomena
Philip Naumann
Jacob R. Kauffmann
G. Montavon
372
0
0
09 May 2025
Uncovering the Structure of Explanation Quality with Spectral Analysis
Johannes Maeß
G. Montavon
Shinichi Nakajima
Klaus-Robert Müller
Thomas Schnake
FAtt
371
0
0
11 Apr 2025
Recent Advances in Malware Detection: Graph Learning and Explainability
Hossein Shokouhinejad
Roozbeh Razavi-Far
Hesamodin Mohammadian
Mahdi Rabbani
Samuel Ansong
Griffin Higgins
Ali Ghorbani
AAML
709
19
0
14 Feb 2025
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
Neural Information Processing Systems (NeurIPS), 2024
Julius Hense
M. J. Idaji
Oliver Eberle
Thomas Schnake
Jonas Dippel
Laure Ciernik
Oliver Buchstab
Andreas Mock
Frederick Klauschen
Klaus-Robert Müller
296
13
0
08 Jan 2025
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
Marvin Sextro
Gabriel Dernbach
Kai Standvoss
S. Schallenberg
Frederick Klauschen
Klaus-Robert Müller
Maximilian Alber
Lukas Ruff
265
1
0
12 Nov 2024
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings
Ashkan Golgoon
Ryan Franks
Khashayar Filom
Arjun Ravi Kannan
386
0
0
01 Nov 2024
Disentangled and Self-Explainable Node Representation Learning
Simone Piaggesi
Andre' Panisson
Megha Khosla
424
0
0
28 Oct 2024
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
Guangrui Yang
Ming Li
Han Feng
Xiaosheng Zhuang
GNN
OOD
BDL
320
11
0
11 Oct 2024
StagedVulBERT: Multi-Granular Vulnerability Detection with a Novel Pre-trained Code Model
IEEE Transactions on Software Engineering (TSE), 2024
Yuan Jiang
Yujian Zhang
Xiaohong Su
Christoph Treude
Tiantian Wang
252
8
0
08 Oct 2024
Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure
Shengjie Xu
Lingxi Xie
205
1
0
23 Sep 2024
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
Information Fusion (Inf. Fusion), 2024
Thomas Schnake
Farnoush Rezaei Jafaria
Jonas Lederer
Ping Xiong
Shinichi Nakajima
Stefan Gugler
G. Montavon
Klaus-Robert Müller
348
11
0
30 Aug 2024
The Clever Hans Effect in Unsupervised Learning
Jacob R. Kauffmann
Jonas Dippel
Lukas Ruff
Wojciech Samek
Klaus-Robert Müller
G. Montavon
SSL
CML
HAI
302
30
0
15 Aug 2024
Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning
Jiajun Zhu
Siqi Miao
Rex Ying
Pan Li
295
2
0
30 Jun 2024
Demystifying Higher-Order Graph Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
Maciej Besta
Florian Scheidl
Lukas Gianinazzi
Grzegorz Kwa'sniewski
S. Klaiman
Jürgen Müller
Torsten Hoefler
586
7
0
18 Jun 2024
Generating Human Understandable Explanations for Node Embeddings
Zohair Shafi
Ayan Chatterjee
Tina Eliassi-Rad
285
1
0
11 Jun 2024
MambaLRP: Explaining Selective State Space Sequence Models
F. Jafari
G. Montavon
Klaus-Robert Müller
Oliver Eberle
Mamba
575
32
0
11 Jun 2024
Explainable Graph Neural Networks Under Fire
Zhong Li
Simon Geisler
Yuhang Wang
Stephan Günnemann
M. Leeuwen
AAML
306
5
0
10 Jun 2024
Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions
Anh Totti Nguyen
Freddy Lecue
Saumitra Mishra
Christopher Pond
Daniele Magazzeni
Manuela Veloso
331
7
0
03 Jun 2024
Explaining Graph Neural Networks via Structure-aware Interaction Index
International Conference on Machine Learning (ICML), 2024
Ngoc H. Bui
Hieu Trung Nguyen
Viet Anh Nguyen
Rex Ying
FAtt
255
15
0
23 May 2024
MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation
Zhaoning Yu
Hongyang Gao
421
9
0
21 May 2024
Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation
International Conference on Information and Knowledge Management (CIKM), 2024
Guojun Liang
Prayag Tiwari
Slawomir Nowaczyk
Stefan Byttner
AI4TS
AI4CE
534
19
0
16 May 2024
Explaining Text Similarity in Transformer Models
Alexandros Vasileiou
Oliver Eberle
262
17
0
10 May 2024
EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
International Conference on Machine Learning (ICML), 2024
Shengyao Lu
Bang Liu
Keith G. Mills
Jiao He
Di Niu
490
7
0
02 May 2024
Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
Zhaoyang Chu
Yao Wan
Qian Li
Yang Wu
Hongyu Zhang
Yulei Sui
Guandong Xu
Hai Jin
AAML
356
33
0
24 Apr 2024
Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
Paulo Yanez Sarmiento
Simon Witzke
Nadja Klein
Bernhard Y. Renard
FAtt
AAML
317
2
0
22 Apr 2024
A Differential Geometric View and Explainability of GNN on Evolving Graphs
International Conference on Learning Representations (ICLR), 2024
Yazheng Liu
Xi Zhang
Sihong Xie
326
4
0
11 Mar 2024
Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
Christian Nauck
M. Lindner
Nora Molkenthin
Jürgen Kurths
Eckehard Scholl
Jorg Raisch
Frank Hellmann
205
4
0
27 Feb 2024
Explaining Predictive Uncertainty by Exposing Second-Order Effects
Florian Bley
Sebastian Lapuschkin
Wojciech Samek
G. Montavon
261
9
0
30 Jan 2024
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
International Conference on Learning Representations (ICLR), 2024
Shengyao Lu
Keith G. Mills
Jiao He
Bang Liu
Di Niu
FAtt
341
16
0
26 Jan 2024
GNNShap: Scalable and Accurate GNN Explanation using Shapley Values
The Web Conference (WWW), 2024
Selahattin Akkas
Ariful Azad
FAtt
394
29
0
09 Jan 2024
Verifying Relational Explanations: A Probabilistic Approach
Abisha Thapa Magar
Anup Shakya
Somdeb Sarkhel
Deepak Venugopal
225
1
0
05 Jan 2024
Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors
Baijun Cheng
Shengming Zhao
Kailong Wang
Meizhen Wang
Guangdong Bai
Ruitao Feng
Yao Guo
Lei Ma
Haoyu Wang
FAtt
AAML
277
12
0
05 Jan 2024
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network
Tong Li
Jiale Deng
Yanyan Shen
Luyu Qiu
Hu Yongxiang
Caleb Chen Cao
246
11
0
23 Dec 2023
Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph
Information Processing & Management (IPM), 2023
Tianqianjin Lin
Kaisong Song
Zhuoren Jiang
Yangyang Kang
Weikang Yuan
Xurui Li
Changlong Sun
Cui Huang
Xiaozhong Liu
298
13
0
10 Dec 2023
Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts
Jonas Jürß
Lucie Charlotte Magister
Pietro Barbiero
Pietro Lio
Nikola Simidjievski
505
1
0
25 Nov 2023
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
Simi Job
Xiaohui Tao
Taotao Cai
Haoran Xie
Lin Li
Jianming Yong
Qing Li
CML
AI4CE
271
24
0
25 Nov 2023
TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
Neural Information Processing Systems (NeurIPS), 2023
Jialin Chen
Rex Ying
AI4TS
324
37
0
30 Oct 2023
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
Jialin Chen
Shirley Wu
Abhijit Gupta
Rex Ying
DiffM
303
8
0
30 Oct 2023
1
2
3
Next
Page 1 of 3
