Study of Topology Bias in GNN-based Knowledge Graphs Algorithms
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Bibliographic record
Abstract
Graph neural networks (GNN) have recently been integrated into knowledge graph representation learning. The efficient message-passing functions in GNNs capture latent relationships between entities within these semantic networks, which aids in various downstream tasks such as link prediction, node classification, and entity alignment. However, there is a general deficiency in representation learning on graphs with loops (cycles) and self-loops<sup>1</sup>. Traditional message-passing functions induce biased learning on knowledge graphs, leading to skewed predictions. This work presents a detailed analysis of representation bias generated by these functions on knowledge graphs containing short and self-loops. We demonstrate the variance in performance on knowledge graphs with varying topology over two downstream: link prediction and entity alignment. The experiments show that the representations from popular learning algorithms are prone to capturing biases in the graphs’ structures. These biases, however, have different effects on the formulated downstream tasks, motivating research in the domain of topology-invariant representation algorithms for knowledge graphs.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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