Centrality-based Interpretability Measures for Graph Embeddings
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
Many real-world data are considered as graphs, such as computer networks, social networks and protein-protein interaction networks. Graph embedding methods are powerful tools for representing large graphs in various domains. A graph embedding method projects the components of a graph, such as its nodes or edges, into a vector space with a lower dimensionality than the adjacency matrix of the graph, and aims to preserve the characteristics of the graph. The generated embedding vectors have been utilized in various graph mining applications such as node classification, link prediction and anomaly detection. Despite the wide success of the graph embedding methods, little study has been done to facilitate a better understanding of the graph embeddings. In this paper, inspired by advancements in interpreting word embeddings, we propose two interpretability measures to quantify the interpretability of graph embeddings by leveraging useful network centrality properties and perform comparisons of different graph embedding methods. Using these scores, we can provide insights into the representational power of graph embedding methods.
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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 | 0.000 | 0.001 |
| 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