CutTheTail: An Accurate and Space-Efficient Heuristic Algorithm for Influence Maximization
Bibliographic record
Abstract
Abstract Algorithmic problem of computing the most influential nodes in an arbitrary graph (influence maximization) is an important theoretical and practical problem and has been extensively studied for decades. For massive graphs (e.g. modelling huge social networks), randomized algorithms are the answer as the exact computation is prohibitively complex, both for runtime and space. This paper concentrates on developing new accurate and efficient randomized algorithms that drastically cut the memory footprint and scale up the computation of the most influential nodes. Implementing the Reverse Influence Sampling method proposed by Borgs, Brautbar, Chayes and Lucier in 2013, we engineered a novel algorithm, CutTheTail (CTT), which solves the problem of influence maximization (IM) while using up to five orders of magnitude smaller space than the existing renown algorithms. CTT is a heuristic algorithm. We tested the accuracy of CTT on large real-world graphs using Monte Carlo simulation as the benchmark and comparing the quality of CTT solution to the algorithms with theoretically proven guaranteed approximation to optimal. Experiments show that CTT provides solutions with the quality equal to the quality of such algorithms. Savings in required space allow to successfully run CTT on a consumer-grade laptop for a graph with almost a billion of edges. To the best of our knowledge, no other IM algorithm can compute a solution on such a scale using a 16 GB RAM laptop.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".