Bibliographic record
Abstract
Social network sites,like Facebook,Twitter,Renren and Sina Weibo,are now becoming increasingly popular on the Internet.For the past few years,numerous research have been made to investigate the topological structure and user behaviors of online social networks, which is quite important for the understanding of human social behaviors,the improvement of current Website systems and the design of online social networks' new applications.This paper provides an overview of online social networks' topology,user behaviors and network evolution. It also summarizes several common measuring methods and typical topological features; highlights user behavior characteristics and their impacts on network topology,and the network evolution.The conclusion can been drawn that as research progresses,the new characteristics of online social networks are gradually recognized and understood:users with a smaller number of correspondents tend to interact more with a subset of correspondents,while users with a very large number of correspondents actually spread their activity evenly across all of the correspondents; users' interactions decrease the clustering coefficient and loose the connections between neighbors;edge creation is influenced by both preferential attachment and proximity bias;small communities tend to merge with large ones which tend to split into two comparable size communities.
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".