Modeling Viral Information Spreading via Directed Acyclic Graph Diffusion
Bibliographic record
Abstract
Viral information like rumors or fake news is spread over a communication network like a virus infection in a unidirectional manner: entity$i$conveys information to a neighbor$j$, resulting in two equally informed (infected) parties. Existing graph diffusion processes focus only on bidirectional diffusion on an undirected graph. Instead, leveraging recent research in graph signal processing (GSP), we propose a new directed acyclic graph (DAG) diffusion process to estimate the probability$x_{i}(t)$of node$i$'s infection at time$t$given an initial infected source node$s$, where$x_{i}(\infty)=1$. Specifically, given an undirected positive graph modeling node-to-node communication, we first estimate its graph embedding: a latent coordinate for each graph node in an assumed low-dimensional manifold space via extreme eigenvectors computed using LOBPCG. Next, we construct a DAG based on Euclidean distances between latent coordinates. Spectrally, we prove that the asymmetric DAG Laplacian matrix contains real non-negative eigenvalues, and that the DAG diffusion converges to the all-infection vector$\mathbf{x}(\infty)=1$as$t\rightarrow\infty$. Simulations show that our DAG diffusion process accurately estimates the probabilities of node infection over a variety of graph structures at different time instants.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".