Efficient and Effective Algorithms for A Family of Influence Maximization Problems with A Matroid Constraint
Bibliographic record
Abstract
Influence maximization (IM) is a classic problem that aims to identify a small group of critical individuals, known as seeds, who can influence the largest number of users in a social network through word-of-mouth. This problem finds important applications including viral marketing, infection detection, and misinformation containment. The conventional IM problem is typically studied with the oversimplified goal of selecting a single seed set. Many real-world scenarios call for multiple sets of seeds, particularly on social media platforms where various viral marketing campaigns need different sets of seeds to propagate effectively. To this end, previous works have formulated various IM variants, central to which is the requirement of multiple seed sets, naturally modeled as a matroid constraint. However, the current best-known solutions for these variants either offer a weak (1/2 - ε )-approximation, or offer a (1 - 1/ e - ε )-approximation algorithm that is very expensive. We propose an efficient seed selection method called AMP, an algorithm with a (1 - 1/ e - ε )-approximation guarantee for this family of IM variants. To further improve efficiency, we also devise a fast implementation, called RAMP. We extensively evaluate the performance of our proposal against 6 competitors across 4 IM variants and on 7 real-world networks, demonstrating that our proposal outperforms all competitors in terms of result quality, running time, and memory usage. We have also deployed RAMP in a real industry strength application involving online gaming, where we show that our deployed solution significantly improves upon the baselines.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".