Representing Player Behaviour via Graph Embedding Techniques: A Case Study in Dota 2
Bibliographic record
Abstract
We explore the use of graph embedding techniques to represent the player behaviour that is expressed in the logs of video games. While such logs hold data that could be useful for personalization, the data is often poorly structured for use with Artificial Intelligence systems and its dimensionality is often high. By using a graph to structure the logs and applying embedding techniques to reduce their dimensionality, a compact vector representation can be obtained that preserves some of their semantics. To explore the potential value of this approach, we obtained gameplay logs from over 3000 matches of Defense of the Ancients 2 (Dota 2) and compared 13 parameter variations of three different embedding techniques: NODE2VEC, LINE, and TGN. Our analysis considers the effects of embedded vector size, dataset size, a step size used for updating vectors as a game proceeds, and different types of player interaction. The results show that NODE2VEC outperforms the other techniques on 7 of the 13 variations that we tested, and that removing one type of player interaction can make it easier to predict the others.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".