GAXG: A Global and Self-Adaptive Optimal Graph Topology Generation Framework for Explaining Graph Neural Networks
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".