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View-based Explanations for Graph Neural Networks

Summary: GVEX introduces Graph Views for GNN explanations, enabling class-specific, queryable insights. Two-tier views (patterns + induced subgraphs) with Sigma2P-hard optimization yield 1/2-approx explain-and-summarize and 1/4-approx single-pass incremental algorithms, scalable to real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6849
Venue
SIGMOD
Year
2024
Pagerank
4.2856106e-05
Overall Rank
9,764 | 32.08%
DOI
10.1145/3639295

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
10,269 Database Views as Explanations for Relational Deep Learning 2026 VLDB 4.1945683e-05
10,792 Graph Compression for Interpretable Graph Neural Network Inference At Scale 2025 VLDB 4.1945683e-05
10,842 ML-Asset Management: Curation, Discovery, and Utilization 2025 VLDB 4.1945683e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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