Datasets:
File size: 5,419 Bytes
a2d45ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # Bitcoin complex-network study — data-processing notes
Derived from: Aspembitova, Feng, Melnikov & Chew (2019), *Fitness preferential attachment as a
driving mechanism in bitcoin transaction network*, PLoS ONE 14(8): e0219346.
Target: extend their framework from 2013 to the 2013–2026 era.
## 1. What the paper did (must be replicated for comparability)
- Node = **user** = cluster of addresses (Reid–Harrigan heuristic: all input addresses of a
multi-input tx belong to one entity). Source: Brugere's dataset, ref [46].
- Edge = one transaction (directed, weighted by BTC amount), **multi-edges retained**.
- Periods: 1P = 2009–2010, 2P = 2011–2012. Scale: 11.4M links, 4.7M nodes (2P).
- Windowing: monthly for assortativity / avg degree / clustering (daily "too sparse");
daily used only for size-vs-price.
- Metrics: in/out/total degree + CCDF, power-law exponent (powerlaw pkg; Clauset–Shalizi–Newman
MLE+KS), global clustering, avg degree, assortativity, active/inactive/dead/resurrected
states, longevity (active months before death), fitness
`x_{i,n} = Δk_{i,n} / Σ_j Δk_{j,n}` (share of new links gained in month n).
- Findings: NO degree PA, NO wealth PA; fitness is power-law (α≈2) and ~constant in time;
corr(fitness, degree)=0.47; corr(age, degree/fitness)≈0.02–0.03; links grow ~linearly with
nodes (no accelerated growth); generative model (fitness PA + birth/death, 2/3 of new nodes
attach to new nodes) reproduces degree distribution but NOT clustering (0.12 vs 0.24).
- Gap: dataset ends 08 Jan 2013. The whole mature era is untouched.
## 2. Measured properties of the modern data (AWS Open Data, day = 2026-09-01)
Schema per tx: txid, block_hash, block_number, block_timestamp, date, index, size, vsize,
lock_time, input_count, output_count, is_coinbase, fee, input_value, output_value,
inputs[] = {address, index, value, type, script_hex/asm, sequence, spent_transaction_hash,
spent_output_index, txinwitness[], required_signatures},
outputs[] = {address, index, value, type, script_hex/asm, required_signatures}
Day 2026-09-01: 627,345 tx | 1,140,645 inputs | 1,462,070 outputs | 142 coinbase |
72,106 multi-input tx (11.5%) | mean in 1.82, mean out 2.33 | 1,010,472 BTC gross out-value.
| issue | measurement | consequence |
|---|---|---|
| input address resolution | 704 / 1,140,645 missing = **99.94% resolved** | clustering heuristic has material |
| output address resolution | 360,057 / 1,462,070 = **24.6% have NO address** | OP_RETURN/data-carrier (ordinals) outputs vanish silently in an address-level graph; ~0% in 2013 |
| change / address reuse | 449,046 / 627,345 = **71.6% of txs share an address on both sides** | after user-clustering most edges become self-loops — must be an explicit policy, report both |
| multi-input (merging power) | 11.5% of txs | only these merge clusters; worth trending over time |
| coinbase | 142/day | flag & exclude or miners are permanent hubs |
## 3. Data-processing decisions to lock before running
1. **Entity level**: address-level (clean baseline, no heuristic) vs user-level (paper-comparable)
vs UTXO-level. Recommend building address-level first, then user-level via clustering.
2. **Clustering guards** (2013 heuristic breaks in 2026): exchange batching and consolidation
create legitimately multi-owner multi-input txs → false mega-clusters; CoinJoin (equal-valued
outputs) merges strangers. Need: cluster-size cap, CoinJoin detection, exchange-address lists,
and a sensitivity analysis over the merge rule.
3. **Self-loop policy**: keep vs drop (affects degree tails); report both.
4. **No-address outputs**: drop, bucket as anonymous, or count as data-carrier activity.
5. **Coinbase**: exclude from the transaction network; keep for issuance/subsidy series.
6. **Weights**: BTC amounts are non-stationary in value → use supply-share or USD for wealth
attachment; keep raw BTC for volume.
7. **Windowing**: monthly for structure; daily only for bursts/event studies. Within-block order
is miner-determined, not chronological — block_timestamp is the finest honest ordering.
8. **Fitness**: needs per-window Δk per node + total Δk; zero-activity nodes excluded; requires
explicit birth/death/resurrection bookkeeping.
9. **Periods**: do not inherit 1P/2P. Run data-driven change-point detection (regimes ~2009-10,
2011-13, 2014-16, 2017, 2018-20, 2021-22, 2023+ ordinals).
10. **Power-law rigor**: MLE + KS + likelihood ratio vs lognormal (powerlaw pkg / CSN); never OLS
on log-log.
11. **Scale**: 1.45B tx / ~2B addresses. Full-graph clustering, path lengths and triangles are
infeasible naively → monthly windowed graphs, streaming/approximate algorithms,
DuckDB + networkit/graph-tool/igraph. The paper's full graph was 11M edges (≈130× smaller).
12. **Reproducibility**: paper's dataset is STILL LIVE at
https://dynamics.cs.washington.edu/nobackup/bitcoin/bitcoin.tar.gz (679 MB, 2018-01-19) →
reproduce first, then extend on AWS parquet (s3://aws-public-blockchain/v1.0/btc/,
2.19 TB total, 146 GB for 2026 YTD).
## 4. Storage/compute plan
- Paper reproduction: 679 MB (fits anywhere, no GPU).
- 2024→now slice: 561 GB (fits in the 972 GB free).
- Full 2.19 TB history: needs external 4 TB drive or query-in-place over HTTP (DuckDB httpfs,
verified working: footer/schema + projection pushdown without full download).
|