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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
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}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,386 | Accelerating Large Scale Real-Time GNN Inference using Channel Pruning | 2021 | VLDB | 8.6490185e-05 |
| 9,922 | View-based Explanations for Graph Neural Networks | 2024 | SIGMOD | 5.1955087e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,597 | Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses | 2024 | VLDB |
| 2 | 5,118 | Efficient Streaming Subgraph Isomorphism with Graph Neural Networks | 2021 | VLDB |
| 3 | 13,292 | Scalable GNN Explanations with Distributed Shapley Values | 2026 | VLDB |
| 4 | 6,354 | On Data-Aware Global Explainability of Graph Neural Networks | 2023 | VLDB |
| 5 | 1,048 | AGL: A Scalable System for Industrial-purpose Graph Machine Learning | 2020 | VLDB |
| 6 | 6,408 | CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression | 2023 | SIGMOD |
| 7 | 1,863 | ByteGNN: Efficient Graph Neural Network Training at Large Scale | 2022 | VLDB |
| 8 | 2,640 | Scalable and Efficient Full-Graph GNN Training for Large Graphs | 2023 | SIGMOD |
| 9 | 9,922 | View-based Explanations for Graph Neural Networks | 2024 | SIGMOD |
| 10 | 10,913 | Inference-friendly Graph Compression for Graph Neural Networks | 2025 | VLDB |