Empirical Assessment of Graph Embedding Techniques for Predicting Missing Links in Biological Networks
Bibliographic record
Abstract
Network science tries to shed light into the complex relationships among entities of a system. For instance, biological networks represent relations between macro molecules such as genes, proteins or other small chemicals. Often potential links are guessed computationally due to expensive nature of wet lab experiments. Conventional link prediction techniques consider local network wiring structure, which may not able to infer true relationships. The recent approaches of graph embedding (or representation learning) aims to capture the complete network structure that may be utilized for link prediction. In this work, we assess the performance of ten (10) state-of-the-art embedding techniques for their effectiveness of link prediction in homogeneous and heterogeneous biological networks. Majority of the graph embedding methods, in its original form, not in a position to predict links. We use the latent representation of the network produce by the embedding methods and recreate the network using various similarity and kernal functions. We evaluate nine (09) such functions in combination with candidate embedding methods. We even compare the performance of five (05) traditional, local structure based link prediction methods to show the superiority. Experimental results clearly reveal that Graph Neural Network (GNN) and Attention based encoders with dot product based decoder are the best performers in predicting missing links for both homogeneous and heterogeneous biological network.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".