Querying Intimate-Core Groups in Weighted Graphs
Bibliographic record
Abstract
The semantic intimacy of relations in many real-world networks (e.g., social, biological, and communication networks) can be modeled by weighted edges in which the more semantically intimate relations between the nodes translate to smaller edge weights. Recently, the problem of community search that aims to find the cohesive groups containing a given set of query nodes has attracted a great deal of attention. However, the bulk of literature on community search problem assumes a simple unweighted input graph, ignoring how semantically intimate the nodes have in retrieved communities. The discovered communities may have a highly cohesive structure, while they perform poorly in the semantic of intimate connections. In this paper, we investigate a novel problem of Querying Intimate-Core Groups (QICG): given a weighted undirected graph G, a set of query nodes Q and a positive integer k, to find a connected subgraph of G in which each node has at least k neighbors, and the sum of weights on its edges is minimum among all such subgraphs. We show that the QICG problem is NP-hard. We develop efficient algorithms based on several practical heuristic strategies to enhance the retrieval efficiency. Extensive experiments are conducted on real-world datasets to evaluate efficiency and effectiveness of proposed algorithms. The results confirm that our intimate-core group model outperforms state-of-the-art models in weighted graphs.
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".