Networks of Bitcoin Investor Wallets
Bibliographic record
Abstract
Bitcoin is a cryptocurrency which has been on the surface lately. Bitcoin bases on a peer-to-peer decentralized network maintained by the bitcoin users. In this thesis we use statistically validated networks method to validate links between bitcoin investor wallets, a bitcoin system equivalent to bank accounts, to identify clusters and understand the investing behavior by characterizing the bitcoin investor wallets. We characterize the investor wallets based on their hourly activity status to study the degree of synchronization in the decision of when to trade and their links. The analysis is based on the bitcoin transaction data from July 2017 to May 2018. The time period was chosen, because in the middle of the analyzed period the bitcoin price reached its highest point so far and then decreased to a quarter of the highest price. \n \nThe study finds that the networks consist of multiple investor wallet clusters, where the wallets have statistically validated links to each other. There is continuity in the behavior of investors between months in terms of the links they have to the other wallets and how they react to the price changes. We also notice, that the investor wallets are likely to transact in the same quantities in different months.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".