Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 1st ACM International Workshop on Complex Networks in Information & Knowledge Management -- CNIKM'09. We are in a connected age: real-world entities often interconnect with each other through explicit or implicit relationships to form a complex network, such as technological networks, social networks, and information networks. They exhibit interesting statistical characteristics like small-world and scale-free. The past decade has witnessed an explosive growth of research on various complex networks. How can we analyze, manage and mine information in large-scale complex networks effectively and efficiently? This gives researchers in database, information retrieval and knowledge management great challenges as well as opportunities. In line with CIKM's tradition of promoting interdisciplinary research, this workshop aims to bring together researchers across both computer science and the emerging network science to foster discussion and exchange ideas. Although these two scientific disciplines speak quite different languages, they certainly can benefit a lot from each other by sharing their concepts, models, techniques, and tools, etc. The call for papers attracted 19 submissions from 12 countries (Australia, Brazil, Canada, China, Germany, India, Korea, Netherlands, Singapore, Sweden, Switzerland and United Kingdom). The program committee accepted 9 full papers and 3 short papers that cover a variety of topics, including community detection, information spread, centrality analysis, link prediction, peer-to-peer networks and recommender systems. In addition, the program includes a keynote speech by Prof. Jure Leskovec of Stanford University on the clustering structure of very large networks. We hope that these proceedings will serve as a valuable reference for researchers and practitioners in this area.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.080 | 0.028 |
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".