Bibliographic record
Abstract
As a generic data structure, graph is capable of modeling complex relations among objects in many real-world problems. Integrated with deep learning and graph signal processing, Graph Neural Network (GNN) has achieved significant progress for solving large, complex, graph-structured problems in recent decade. GNNs extend basic Neural Network (NN) by incorporating graph structures grounded on the relational inductive bias and have been commonly believed to outperform NNs in real-world tasks. Despite their efficacy, the development of deep and shallow GNNs is confronting two main challenges,• Limited expressive power of deep GNNs: Since graph convolution can be considered as a special form of Laplacian smoothing, stacking multiple GNN layers like the way as deep NNs can lead to an over-smoothing issue, where distant nodes become less identifiable and hard to be discriminated;• Performance degradation of shallow GNNs on heterophilic graphs: When the homophily principle is absent and nodes from different classes are more likely to be connected, the representation of nodes from distinct classes will be erroneously blending, leading nodes to be indistinguishable.In this dissertation, we will delve into these two obstacles in depth, analyzing themthoroughly and proposing methods to address them efficiently.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".