Graph Compression for Interpretable Graph Neural Network Inference At Scale
Summary: ExGIS compresses a graph once into a queryable structure that supports exact inference for any bounded-depth GNN without decompression. Its parallel engine balances workloads while producing faithful explanatory subgraphs, enabling scalable, interactive analysis. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yangxin Fan (Case Western Reserve University)
- 2. Haolai Che (Case Western Reserve University)
- 3. Mingjian Lu (Case Western Reserve University)
- 4. Yinghui Wu (Case Western Reserve University)
BibTeX Citation
@article{fan_vldb25,
title = {{Graph Compression for Interpretable Graph Neural Network Inference At Scale}},
author = {Fan, Yangxin and Che, Haolai and Lu, Mingjian and Wu, Yinghui},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5239--5242},
doi = {10.14778/3750601.3750641},
url = {https://doi.org/10.14778/3750601.3750641},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,439 | Accelerating Large Scale Real-Time GNN Inference using Channel Pruning | 2021 | VLDB | 8.4652234e-05 |
| 10,105 | View-based Explanations for Graph Neural Networks | 2024 | SIGMOD | 5.0789354e-05 |
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