A Fast Local Community Detection Algorithm in Signed Social Networks
Bibliographic record
Abstract
Recent years have witnessed the rapid growth of social network services and consequently, research problems investigated in this area. Community detection is one of the most important problems in social networks. A good community can be defined as a group of vertices that are highly connected and loosely connected to the vertices outside the group. Community detection includes exploring the community partitioning in social networks. Regarding the fact that social networks are huge, having complete information about the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. This problem can be defined as the detection of a community for a given node by using local information. Many networks contain both positive and negative relations. A community in signed networks is defined as a group of nodes that are densely connected by positive links within the community and negative links between communities. In this paper, considering the problem of local community detection in signed networks, a new fast algorithm, noted as$Alg_{SP}$, is developed to identify a dense community for a given node in signed networks. Experimental results show that the proposed algorithm can detect the ground-truth communities independently from the starting nodes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".