Temporal Graph Representation Learning via Maximal Cliques
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) have been proposed to learn graph representations for various graph mining tasks such as link prediction and node classification. These methods aggregate information from neighbors of a node to generate the node representation vector. Temporal GNN models consider the temporal and neighborhood information of nodes. However, few temporal GNN methods consider network substructures such as triads and cliques. In this paper, we present a temporal GNN-based method that generates node embeddings by aggregating neighbors of a node that exist in the maximal cliques of the graph containing the node. The reason for considering neighbors that form a maximal clique with the target node is that nodes in a maximal clique are highly connected to each other and most likely share similar characteristics. In addition, we consider the time dependency of nodes by generating temporal walks on the cliques such that in these walks the time order of the nodes is respected. The node embedding is based on the aggregation of the node’s temporal walks. Our experiments on seven datasets show the effectiveness of our method in both link prediction and node classification tasks. Furthermore, our method is faster than other baselines we evaluate.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.010 | 0.008 |
| 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