Node Copying for Protection Against Graph Neural Network Topology\n Attacks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Adversarial attacks can affect the performance of existing deep learning\nmodels. With the increased interest in graph based machine learning techniques,\nthere have been investigations which suggest that these models are also\nvulnerable to attacks. In particular, corruptions of the graph topology can\ndegrade the performance of graph based learning algorithms severely. This is\ndue to the fact that the prediction capability of these algorithms relies\nmostly on the similarity structure imposed by the graph connectivity.\nTherefore, detecting the location of the corruption and correcting the induced\nerrors becomes crucial. There has been some recent work which tackles the\ndetection problem, however these methods do not address the effect of the\nattack on the downstream learning task. In this work, we propose an algorithm\nthat uses node copying to mitigate the degradation in classification that is\ncaused by adversarial attacks. The proposed methodology is applied only after\nthe model for the downstream task is trained and the added computation cost\nscales well for large graphs. Experimental results show the effectiveness of\nour approach for several real world datasets.\n
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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) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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