GAXG: A Global and Self-Adaptive Optimal Graph Topology Generation Framework for Explaining Graph Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Numerous explainability techniques have been developed to reveal the prediction principles of Graph Neural Networks (GNNs) across diverse domains. However, many existing approaches, particularly those concentrating on model-level explanations, tend to grapple with the tunnel vision problem, leading to less-than-optimal outcomes and constraining users' comprehensive understanding of GNNs. Furthermore, these methods typically require hyperparameters to mold the explanations, introducing unintended human biases. In response, we present GAXG, a global and self-adaptive optimal graph topology generation framework for explaining GNNs' prediction principles at model-level. GAXG addresses the challenges of tunnel vision and hyperparameter reliance by integrating a strategically tailored Monte Carlo Tree Search (MCTS) algorithm. Notably, our tailored MCTS algorithm is modified to incorporate an Edge Mask Learning and Simulated Annealing-based subgraph screening strategy during the expansion phase, resolving the inherent time-consuming challenges of the tailored MCTS and enhancing the quality of the generated explanatory graph topologies. Experimental results underscore GAXG's effectiveness in discovering global explanations for GNNs, outperforming leading explainers on most evaluation metrics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it