Security Games on Social Networks.
Bibliographic record
Abstract
Many real-world problems exhibit competitive situations in which a defender (a defending agent, agency, or organization) has to address misinformation spread by its adversary, e.g., health organizations cope with vaccination-related misinformation provided by anti-vaccination groups. The rise of social networks has allowed misinformation to be easily and quickly diffused to a large community. Taking into account knowledge of its adversary’s actions, the defender has to seek efficient strategies to limit the influence of the spread of misinformation by the opponent. In this paper, we address this problem as a blocking influence maximization problem using a game-theoretic approach. Two players strategically select a number of seed nodes in the social network that could initiate their own influence propagation. While the adversary attempts to maximize its negative influence, the defender tries to minimize this influence. We represent the problem as a zero-sum game and apply the Double Oracle algorithm to solve the game in combination with various heuristics for oracle phases. Our experimental results reveal that by using the game theoretic approach, we are able to significantly reduce the negative influence in comparison to when the defender does not do anything. In addition, we propose using an approximation of the payoff matrix, making the algorithms scalable to large real-world networks.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".