MOSER: Scalable Network Motif Discovery Using Serial Test
Bibliographic record
Abstract
Given a graph G , a motif (e.g., 3-node clique) is a fundamental building block for G. Recently, motif-based graph analysis has attracted much attention due to its efficacy in tasks such as clustering, ranking, and link prediction. These tasks require Network Motif Discovery (NMD) at the early stage to identify the motifs of G. However, existing NMD solutions have two drawbacks: (1) Lack of theoretical guarantees on the quality of the samples generated using the existing methods, and (2) inefficient algorithms, which are not scalable for large graphs. These limitations hinder the exploration of motifs for analyzing large graphs. To address the above issues, we propose a novel solution named MOSER ( MO tif Discovery using SER ial Test). This novel NMD framework leverages a significance testing method known as the serial test, which differs from the existing solutions. We further propose two fast incremental subgraph counting algorithms, allowing MOSER to scale to larger graphs than ever possible before. Extensive experimental results show that using MOSER can improve the state-of-the-art up to 5 orders of magnitude in efficiency and that the motifs found by MOSER facilitate downstream tasks such as link prediction.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".