Dealing with Disappearance of an Actor Set in Social Networks
Bibliographic record
Abstract
Social networks are dynamic structures that contain a set of entities and links. In such a dynamic environment, a specific node or a group of nodes can play an important role in the information flow transmission within the network and therefore, its disappearance may lead to a disconnected network or a breakdown in the information flow. The objective of this paper is to extend our previous work on managing a node disappearance to handling the disappearance of a group of nodes. The proposed approach relies on the role played by a group of nodes to conduct network changes and maintain the network connected while restoring the information flow with a similar quality as before the group disappearance. We consider two situations (only one versus many communities) and categorize groups of nodes into three classes (scattered, contiguous and hybrid). Hence, we manage a group disappearance with respect to its class and the network topology by adding new links in a parsimonious way and finding a substitute for a leaving group. Our approach differs from existing link prediction solutions by the fact that it uses the information flow quality as a key performance indicator to identify the potential links to add and/or the possible substitute to a disappearing group. We implement a prototype by using an open source social network analysis library (NetworkX) and we validate our solution through experiments. The results show the benefits of our solution in terms of response time and the number of added links.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".