K-Truss Decomposition of Large Networks on a Single Consumer-Grade Machine
Bibliographic record
Abstract
k-truss decomposition of a graph is a method to discover cohesive subgraphs and to study the hierarchical structure among them. The existing algorithms for computing k-truss of today's massive networks mainly focus on reducing the runtime using parallel computation on a powerful multi-core server. Our focus, by contrast, is to investigate the feasibility of computing the k-truss on a single consumer-grade machine within a reasonable amount of time. We engineer two efficient k-truss decomposition algorithms: the edge-peeling algorithm proposed by J. Wang and J. Cheng and the asynchronous h-index-updating algorithm proposed by A. E. Sariyuce, C. Seshadhri, and A. Pinar. We reduce their memory usage significantly by optimizing the underlying data structures and by using WebGraph, an efficient framework for graph compression. With our optimized implementation, we show that we can efficiently compute k-truss decomposition of large networks (e.g., a graph with 1.2 billion edges) on a single consumer-grade machine.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".