Node Embedding using Mutual Information and Self-Supervision based\n Bi-level Aggregation
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
Graph Neural Networks (GNNs) learn low dimensional representations of nodes\nby aggregating information from their neighborhood in graphs. However,\ntraditional GNNs suffer from two fundamental shortcomings due to their local\n($l$-hop neighborhood) aggregation scheme. First, not all nodes in the\nneighborhood carry relevant information for the target node. Since GNNs do not\nexclude noisy nodes in their neighborhood, irrelevant information gets\naggregated, which reduces the quality of the representation. Second,\ntraditional GNNs also fail to capture long-range non-local dependencies between\nnodes. To address these limitations, we exploit mutual information (MI) to\ndefine two types of neighborhood, 1) \\textit{Local Neighborhood} where nodes\nare densely connected within a community and each node would share higher MI\nwith its neighbors, and 2) \\textit{Non-Local Neighborhood} where MI-based node\nclustering is introduced to assemble informative but graphically distant nodes\nin the same cluster. To generate node presentations, we combine the embeddings\ngenerated by bi-level aggregation - local aggregation to aggregate features\nfrom local neighborhoods to avoid noisy information and non-local aggregation\nto aggregate features from non-local neighborhoods. Furthermore, we leverage\nself-supervision learning to estimate MI with few labeled data. Finally, we\nshow that our model significantly outperforms the state-of-the-art methods in a\nwide range of assortative and disassortative graphs.\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) | 0.000 | 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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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