Finding Antagonistic Communities in Signed Uncertain Graphs
Bibliographic record
Abstract
Many real-world networks are signed networks with positive and negative edge weights, such as social networks with positive (friend) or negative (foe) relationships between users, and gene interaction networks with positive (stimulatory) or negative (inhibitory) interactions between genes. A well-known data mining task in signed networks is to find groups of antagonistic communities, where the vertices in the same community have a strong positive relationship and the vertices in different communities have a strong negative relationship. Most existing methods find antagonistic communities by modelling a signed network as a static graph with constant positive and negative edge weights. However, since the relationship between vertices is often uncertain in many real-world networks, it is more practical and accurate to capture the uncertainty of the relationship in the network by a signed uncertain graph (SUG), where each edge is independently associated with a discrete probability distribution of signed edge weights. How to find groups of antagonistic communities in a SUG is a challenging data mining task that has not been systematically tackled before. In this paper, we propose a novel method to tackle this task. We first model a group of antagonistic communities by a set of subgraphs, where the vertices in the same subgraph have a large expectation of positive edge weights and the vertices in different subgraphs have a large expectation of negative edge weights. Then, we propose a method to efficiently find significant groups of antagonistic communities by restricting all the computations on small local subgraphs of the SUG. Extensive experiments on seven real-world datasets and a synthetic dataset demonstrate the outstanding effectiveness and efficiency of the proposed method.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".