vGraph: A Generative Model for Joint Community Detection and Node\n Representation Learning
Bibliographic record
Abstract
This paper focuses on two fundamental tasks of graph analysis: community\ndetection and node representation learning, which capture the global and local\nstructures of graphs, respectively. In the current literature, these two tasks\nare usually independently studied while they are actually highly correlated. We\npropose a probabilistic generative model called vGraph to learn community\nmembership and node representation collaboratively. Specifically, we assume\nthat each node can be represented as a mixture of communities, and each\ncommunity is defined as a multinomial distribution over nodes. Both the mixing\ncoefficients and the community distribution are parameterized by the\nlow-dimensional representations of the nodes and communities. We designed an\neffective variational inference algorithm which regularizes the community\nmembership of neighboring nodes to be similar in the latent space. Experimental\nresults on multiple real-world graphs show that vGraph is very effective in\nboth community detection and node representation learning, outperforming many\ncompetitive baselines in both tasks. We show that the framework of vGraph is\nquite flexible and can be easily extended to detect hierarchical communities.\n
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".