Design Improvements for Message Propagation in Malleable Social Networks
Bibliographic record
Abstract
This study presents the formal problem definition and computational analysis of the network design improvements for idea and message propagation in both enterprise and consumer social networks (ESN and CSN, respectively). Message propagation in social networks is impacted by how messages are seeded in the network, and by propagation characteristics of the network topology itself. It has been recognized that the propagation properties of these networks can be actively influenced by network design interventions, such as the deliberate creation of new connections. We address the problem of finding cost‐effective message seeding, and identifying potential new network connections that allow improved propagation in social networks with cascade propagation. We use the hop‐constrained minimum spanning tree (HMST) model to find the seeds and possible new connections that result in networks with improved propagation properties. Moreover, we present new heuristic algorithms that substantially improve the solution quality for the HMST problem. Computational results posit that the design improvements proposed by the HMST approach can greatly improve cascade propagation performance of the networks at low cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".