High-precision shortest distance estimation for large-scale social networks
Bibliographic record
Abstract
Over the past decades, many large-scale social network systems, such as Facebook and Twitter, have been deployed in different countries. How to efficiently analyze the topological characteristics of large-scale social networks has been a challenging problem in the research community. One of the critical topological characteristics is the shortest distance between two nodes in a network. The existing shortest distance algorithms, such as Breadth First Search (BFS), work well with small networks. For a network with billions of nodes, calculating the pairwise shortest distances with these algorithms requires an overlong period of time. In this paper, we present a high-precision ShOrtest Distance Approximation (SODA) scheme, which utilizes a small set of pre-calculated distances to estimate the shortest distance between each pair of nodes in large-scale social networks. Compared with the existing shortest distance estimation schemes for social networks, SODA leads to high estimation accuracy since it utilizes a novel optimization method, Robust Discrete Matrix Decomposition (RDMD), to eliminate the impact of significant errors/outliers and generate the coordinates of the nodes in a network simultaneously. In addition, SODA differentiates the asymmetric distances in directed graphs. Consequently, SODA works well with both directed and undirected social networks. Finally, SODA only involves convex optimization. Therefore, SODA is highly competitive in terms of computation complexity. Our experimental results indicate that SODA outperforms the state-of-the-art shortest distance estimation schemes in terms of estimation accuracy and running time.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".