USIWO: A Local Community Search Algorithm for Uncertain Graphs
Bibliographic record
Abstract
Community detection and community search are both critical tasks in graph mining, each serving unique purposes and presenting distinct challenges. The former aims to partition the graph vertices into densely connected subsets, while the latter adopts a more ego-centric approach, focusing on a specific node or group of nodes to identify a densely-connected subgraph that contains these query nodes. However, many real-world networks are characterized by uncertainty, leading to the notion of uncertain or probabilistic graphs. The transition from deterministic graphs to uncertain graphs introduces new challenges. We present USIWO, an efficient and practical solution for community search in unweighted uncertain graphs with edge uncertainty. In addition to being accurate, the approach utilizes an efficient data structure for storing only the relevant parts of the network in main memory, eliminating the need to store the entire graph, making it a valuable tool in finding the core of a community on very large uncertain graphs, when there is limited time and memory available. The algorithm operates through a one-node-expansion approach, based on the concepts of strong and weak links within a graph. Experimental results on several datasets demonstrate the algorithm's efficiency and performance.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".