GCNFusion: An efficient graph convolutional network based model for information diffusion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Investigating the dynamics of spreading processes in real-world applications such as pathogen spread prediction, marketing, political events, etc has attracted the attention of researchers from a variety of fields. Influence-based information diffusion is one convincing attempt to solve the information diffusion problem. In this regard, most of the attempts suffer from certain drawbacks such as complexity, dependency on the underlying diffusion model, or low prediction accuracy. We have looked at this problem from a fresh perspective and come up with an innovative solution for solving it. Our hybrid approach falls at the intersection of three research areas: feature selection, graph embedding, and information dissemination. To discover the influential nodes in a network, we develop a method comparable to wrapper methods in feature selection, in which we employ the strength of graph convolutional neural networks (GCNs). The results of our implementation in Python on five datasets Cora, Email, Hamster, Router, and CEnew, under the susceptible–infected–recovered (SIR) model, approved that GCNFusion exceptionally outperforms benchmark methods by respectively around 3%, 5%, 5%, 2%, and 3%. Furthermore, the proposed method is a decent suit for real-world applications on complex networks due to its low computational complexity.
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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 | 0.001 | 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 it