Bitcoin Research with a Transaction Graph Dataset

Hugo Schnoering; Michalis Vazirgiannis

REVIEW 3 major objections 5 minor 28 references

Bitcoin Research with a Transaction Graph Dataset

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The authors release a 252-million-node, 785-million-edge temporal Bitcoin transaction graph claimed to be the largest public dataset of its kind, with entity labels and graph-neural-network baselines.

desk verdict A genuinely large, usable Bitcoin transaction graph with a label set that is currently unvalidated; the resource deserves review, but the labeling pipeline needs real error analysis before it becomes a benchmark. read the letter →

arxiv 2411.10325 v1 pith:UC2MIJX5 submitted 2024-11-15 cs.LG q-fin.GN

Hugo Schnoering , Michalis Vazirgiannis This is my paper · ORCID

classification cs.LGq-fin.GN

keywords Bitcointransactiongraphblockchaindatasetneuralnetworksentitylabelingtemporallanguagemodelannotationcryptocurrencyanalysis

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces a large-scale temporal transaction graph of Bitcoin, built from the first 700,000 blocks, in which nodes are clusters of locking scripts (entities) and edges are value transfers between them. The authors claim this is the largest publicly available Bitcoin transaction dataset, with 252 million nodes and 785 million edges, spanning nearly 13 years and 670 million transactions. To make the graph useful for supervised learning, they label about 34,000 nodes by entity type using addresses harvested from BitcoinTalk forum posts via a large language model, plus supplementary official and research sources. They then train four graph neural networks and a gradient boosting classifier to predict node labels, establishing baseline macro-F1 scores up to 0.64. If the dataset is sound, it lowers the barrier to Bitcoin graph research by removing the need to build and cluster the blockchain graph from scratch.

What carries the argument

The central object is the transaction graph itself, built by a three-stage pipeline: (1) parse the Bitcoin blockchain into script-level transactions, (2) cluster locking scripts into entity nodes using heuristics from prior work, excluding CoinJoin and colored-coin transactions to avoid poisoning clusters and edges, and (3) define directed edges from net value flow between sender and recipient aliases. The labeling machinery is a set of ChatGPT prompts that process BitcoinTalk posts together with transaction and price context, returning reasoning, entity names, and addresses, which are then mapped to a taxonomy of eleven entity types (individual, mining, exchange, marketplace, gambling, bet, faucet, mixer, ponzi, ransomware, bridge). The same addresses are propagated to scripts and clusters, and clusters with conflicting labels are left unlabeled.

What would settle it

Take a random sample of, say, 200 labeled addresses from the BitcoinTalk-derived portion, have a human expert verify the entity name and type against the linked forum posts and on-chain behavior, and compute precision; if precision drops below a plausible threshold (e.g., 80%), or if the errors are systematically concentrated in a few entity types, the dataset's validity as ground truth for supervised learning is undermined.

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Extended reading notes

Core claim

The central claim is that this is the largest publicly available Bitcoin transaction graph dataset designed for research, overcoming limitations of existing datasets such as Elliptic 1 and 2. The graph represents real entities rather than raw addresses: nodes are clusters of locking scripts grouped by heuristics that identify likely co-ownership, and directed edges represent value transfers computed from net value flows. Each node and edge carries a block-index timestamp, making the graph temporal. A labeling pipeline uses the language model ChatGPT to extract Bitcoin addresses from BitcoinTalk posts and infer the entity name and type, supplemented by address lists from exchanges, ransomware researchers, the U.S. sanctions list, mining coinbase messages, and the Wrapped Bitcoin bridge; nodes are labeled when their constituent scripts match labeled addresses and conflicts are resolved by discarding ambiguous clusters. As technical validation, node classification experiments show that graph neural networks (GAT and GIN) reach macro-F1 scores around 0.63-0.64, outperforming a gradient boosting classifier on tabular features, indicating that node features and graph structure carry predictive signal.

Load-bearing premise

The dataset's supervised value rests on the assumption that labels extracted by the language model and mapped through the pipeline are accurate, yet no manual validation, precision, or recall estimate is provided for the ChatGPT-based labeling.

Editorial extensions

If this is right

Researchers can directly benchmark graph learning methods on a temporal Bitcoin graph without building their own blockchain parser and clustering pipeline.
The temporal edge/node timestamps enable studies of network evolution, entity interaction dynamics, and event-driven behavior over nearly 13 years.
The 101k labeled addresses and 34k labeled nodes provide supervised signals for entity-type classification, and the 14M BitcoinTalk posts are included as a text corpus.
The dataset's scale (252M nodes, 785M edges) positions it as a pre-training resource for transaction-graph representation learning that could transfer to other payment networks.
The baseline results suggest that graph structure helps entity-type prediction, with GNNs outperforming tabular classifiers on several classes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The labeling approach, if validated, implies that large language models can be used to semi-automatically build entity-identity ground truth from community forums, a method transferable to other blockchain ecosystems or other domains with public discussion and pseudonymous identifiers.
The paper's claim of being 'largest' depends on its definition of publicly available and graph-form; a natural test is a systematic comparison with other released datasets' sizes and label quality, which the paper does not perform.
Because the labels are derived from the same BitcoinTalk posts, there may be a temporal selection bias: entities active after the rise of LLM-era posts or those that announce themselves on forums are over-represented, affecting class distributions.
The confusion of many classes toward 'individual' suggests that the frontier between individual users and organizational addresses is fundamentally fuzzy, which could motivate multi-label or hierarchical labeling in future versions.

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Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The headline is the dataset: 252M nodes, 785M timestamped edges, built from the first 700,000 blocks with standard clustering heuristics, plus code and data released. That is a real contribution. Elliptic and Elliptic2 are smaller and binary-labeled; this is the first public graph at this scale with 13 years of temporal information and multi-class entity labels. The graph construction is careful—CoinJoin and colored-coin exclusions are sensible, and the node/edge schema is well documented. The GNN baselines are not novel, but they are competent and provide a reasonable starting point for future work. For a data descriptor, that is enough.

The soft spots are real but not fatal to the resource’s core value. The density claim is wrong by orders of magnitude: 785M edges in a graph with 252M nodes gives density around 1e-8, not 1%. That error needs correcting. The address counts also do not add up—the category table sums to ~103,600, while the text claims 101,186. These are the kind of sloppy numbers that erode trust in a dataset paper.

The bigger issue is the label set. 86% of addresses come from BitcoinTalk posts processed by gpt-4o-mini with no manual validation, no precision/recall, no error estimates. The paper’s own limits section concedes forum misinformation and ChatGPT’s limitations. The technical validation, which uses the labels as ground truth to train GNNs and reports macro-F1 around 0.6, is circular: it shows the models can fit the labels, not that the labels are correct. Without a randomly sampled human audit of a few hundred addresses, the supervised benchmark value is unproven. That said, the labels are clearly useful for exploratory analysis and for entity resolution heuristics; the risk is mainly for researchers who treat the labels as gold for training classifiers.

Who is this for? Anyone working on Bitcoin graph analysis, anti-money-laundering, or large-scale temporal GNN benchmarks. The paper is worth sending to review, but a serious referee should require the factual corrections, a label-quality audit, and error bars on the baselines before acceptance. I would not desk-reject it; the dataset itself is significant and the flaws are fixable.

Referee Report

3 major / 5 minor

Summary. The paper introduces a large-scale Bitcoin transaction graph dataset, with 252 million nodes (clusters of locking scripts) and 785 million directed edges, spanning the first 700,000 blocks (~13 years) and 670 million transactions. Each node and edge carries temporal and aggregate features. Roughly 34,098 nodes are labeled by entity type, derived from about 101,186 labeled Bitcoin addresses. Address labels were obtained primarily by running ChatGPT (gpt-4o-mini) over BitcoinTalk forum posts to infer entity names and types, supplemented by several external sources (CoinMarketCap, DefiLlama, ransomware datasets, SDN list, etc.). The authors train GCN, GraphSage, GAT, GIN, and a gradient boosting classifier on a node classification task and report macro-F1 scores between 0.56 and 0.64 as baselines. The dataset and code are released publicly.

Significance. If the data and labels are reliable, this is a potentially valuable community resource: it appears to be the largest public Bitcoin transaction graph with temporal information, node/edge attributes, and entity-type labels, and it also bundles the raw BitcoinTalk posts used for labeling. The authors provide code for graph construction, labeling, and prediction, which supports reproducibility. The main significance hinges on whether the automatically generated labels can serve as ground truth for supervised graph learning and for downstream analyses such as entity interaction studies and temporal evolution analysis. The scale and the public availability alone make the dataset a candidate benchmark, but the lack of label-quality evidence currently leaves the central claim of a reliable supervised benchmark unproven.

major comments (3)

[Node labels / Limits / Technical Validation] The paper's central claim of providing a labeled dataset for supervised graph learning rests on the quality of the labels produced by the ChatGPT-based pipeline (Section 'Node labels', subsection 'ChatGPT' and 'Prompts'). The 'Limits' paragraph concedes that forum posts may contain misinformation and that ChatGPT has limitations in inferring entities from short unstructured context, yet no manual validation, precision/recall estimates, or agreement statistics are provided. The 'Technical Validation' section uses macro-F1 scores of GNNs (Table 5) as evidence that 'the dataset’s reliability and relevance' is established, but this reasoning is circular: models trained and evaluated on the same automatically generated labels can fit those labels without demonstrating their correctness. An external gold standard, a human-annotated sample, or at least a reported precision estimate on a stratified subset is needed to support the use of these labels as ground truth.
[Data Records] The statement in 'Data Records' that 'The graph’s density is approximately 1%' is incorrect by many orders of magnitude. With 252,219,007 nodes and 785,954,737 edges, the directed density is E/(V(V-1)) ≈ 1.2 × 10^-8, or about 0.0000012%, not 1%. This is not a minor typo: it materially misrepresents the graph's sparsity and may mislead users about the expected connectivity and the feasibility of algorithms. The sentence should be corrected, or the intended quantity (e.g., density of a sampled subgraph) should be defined explicitly.
[Node labels / Graph Construction] The chain from an address label to a node label involves two steps that are not quantitatively validated: (i) the address-to-script mapping and the clustering heuristics (ref. 8) that group scripts into nodes, and (ii) the resolution of conflicting labels within a cluster, where the paper states that 'no label was assigned' but gives no statistics on how often conflicts occur or how many otherwise-valid addresses are discarded. Since the labeled nodes are the core of the supervised benchmark, the paper should report the accuracy of the clustering heuristics on a known set of addresses (e.g., addresses of well-known services) and the number of clusters affected by label conflicts. Without these numbers, the reliability of the 34,098 labeled nodes is not established.

minor comments (5)

[Abstract / Data Records] The abstract states '33,000 nodes' and 'nearly 100,000 Bitcoin addresses', while the Data Records section reports 34,098 labeled nodes and 101,186 labeled addresses (Table 3). The counts should be harmonized, and the abstract should use the final figures.
[Table 1] In Table 1, the column labeled 'b' is described as 'Node alias of the sender', which appears to be a copy-paste error; it should read 'Node alias of the recipient' (or 'receiver').
[Background & Summary / Usage Notes] There are several typographical and grammatical errors that should be fixed, including 'econonomy' (Background), 'databse' (Usage Notes), and 'is the is the username' (Usage Notes).
[Node labels / Prompts] The prompt templates are said to be available in the code repository, but the paper itself does not include any sample prompt or output. Including a short example in the appendix would help readers understand the extraction and inference process without needing to inspect the code.
[Methods / Graph Construction] The heuristics used for CoinJoin detection and colored-coin detection are cited from the first author's prior work (refs. 9 and 10, respectively). A brief description of the heuristics' expected false-positive/false-negative rates, or at least a statement of whether they have been validated independently, would increase confidence in the graph construction.

Circularity Check

1 steps flagged · score 4.0 of 10

Self-referential label validation: GNN macro-F1 on ChatGPT-derived labels is presented as evidence of dataset reliability, but the labels are both the supervision and the evaluation target.

fitted input called prediction [Technical Validation, first paragraph]
"Predicting the label of a node based on its attributes serves as a validation method for our dataset. This approach establishes a robust connection between off-chain data, the entity type behind a cluster, and on-chain activity data. The successful prediction of these labels underscores the dataset’s reliability and relevance"

The labels used as ground truth are the same labels produced by the ChatGPT/BitcoinTalk labeling pipeline; the GNN is trained and evaluated on this same 'label' column. The reported macro-F1 therefore measures the model's ability to reproduce the pipeline's own labels from on-chain features, not the correctness of those labels. Presenting this self-consistency as 'underscores the dataset's reliability' is circular because the validation target is the very quantity whose reliability is at issue. The paper's own 'Limits' paragraph concedes that BitcoinTalk posts may contain misinformation and that ChatGPT has limitations in inferring entities from short context, and no external gold standard, precision, or recall estimate is provided to break the circle.

full rationale

The paper's central contribution is a dataset, not a formal derivation, so most of the construction chain is not circular. The clustering and CoinJoin detection heuristics are cited from the first author's prior work (refs 8 and 9), but those are methodological citations to externally testable procedures, not an argument whose conclusion reduces to the citation; they do not by themselves constitute circularity. The labeled addresses also include independent external sources (Padua, Montreal, SDN, CoinMarketCap, DefiLlama, WBTC), which provide some non-self-referential label content. The main circular step is the Technical Validation: the authors fit GNNs to the 'label' column and then treat the resulting macro-F1 as evidence that the dataset is reliable. Since the labels are both the supervision and the evaluation target, the F1 only shows that on-chain features correlate with the ChatGPT/forum-derived labels, not that those labels are true. The paper's own 'Limits' paragraph explicitly acknowledges possible inaccuracies in BitcoinTalk posts and ChatGPT's limitations, reinforcing that this validation is self-consistency rather than external confirmation. Because the largest-dataset claim and the external label sources are independent of this self-referential validation, the circularity is partial rather than total; the dataset itself is not derived from its own predictions.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

There are no mathematical axioms or invented physical entities in this dataset paper. The ledger emphasizes the assumptions about data quality and heuristics that the central claims rely on: the clustering heuristics, the coinjoin removal heuristics, and the LLM-based labeling pipeline, several of which are self-citations.

assumptions (5)

domain assumption The heuristic clustering rules from Schnoering et al. (2024) group Bitcoin scripts into entities with sufficient accuracy.
The graph nodes are script clusters produced by heuristics from the first author's prior paper; if clustering errors are large, the node semantics break. No error analysis of the clustering is provided.
domain assumption The heuristics for detecting CoinJoin and colored coin transactions correctly identify all such transactions.
Excluded transactions are detected by heuristics; missed CoinJoins would pollute edges and clustering, but no precision/recall of these detectors is reported.
domain assumption ChatGPT (gpt-4o-mini) correctly extracts Bitcoin addresses and infers entity types from BitcoinTalk posts.
The labeling pipeline has no manual verification or agreement metric; the paper's Limits section acknowledges this weakness.
domain assumption BitcoinTalk posts are truthful and representative of real Bitcoin entities.
Labels are largely derived from user-contributed forum content, which may contain errors, spam, or fake claims.
domain assumption Block index is a valid timestamp proxy, and the first 700,000 blocks capture the relevant Bitcoin history.
Block index is used as timestamp for all temporal attributes; the cut at block 700,000 is a completeness choice.

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Cite this review

Pith. "Pith review of Bitcoin Research with a Transaction Graph Dataset." pith.science (2026). https://pith.science/paper/UC2MIJX5

@misc{pith2026241110325,
  author       = {Pith},
  title        = {Pith review of: Bitcoin Research with a Transaction Graph Dataset},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UC2MIJX5}},
  note         = {Machine review of arXiv:2411.10325}
}

read the original abstract

Bitcoin, launched in 2008 by Satoshi Nakamoto, established a new digital economy where value can be stored and transferred in a fully decentralized manner - alleviating the need for a central authority. This paper introduces a large scale dataset in the form of a transactions graph representing transactions between Bitcoin users along with a set of tasks and baselines. The graph includes 252 million nodes and 785 million edges, covering a time span of nearly 13 years of and 670 million transactions. Each node and edge is timestamped. As for supervised tasks we provide two labeled sets i. a 33,000 nodes based on entity type and ii. nearly 100,000 Bitcoin addresses labeled with an entity name and an entity type. This is the largest publicly available data set of bitcoin transactions designed to facilitate advanced research and exploration in this domain, overcoming the limitations of existing datasets. Various graph neural network models are trained to predict node labels, establishing a baseline for future research. In addition, several use cases are presented to demonstrate the dataset's applicability beyond Bitcoin analysis. Finally, all data and source code is made publicly available to enable reproducibility of the results.

Figures

Figures reproduced from arXiv: 2411.10325 by the authors.

**Figure 1.** Schematic of a transaction ∆. Nodes with a single (resp. double) border represent TXOs (resp. transactions). TXOs consumed by ∆ originate from prior transactions, while those created in ∆ may serve as input TXOs in subsequent transactions. 2/15 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Labeling pipeline. purposes and motivations behind Bitcoin usage. Bitcoin users are identified by randomly generated addresses. Information from the blockchain alone is insufficient to ascertain the true identity or nature of the entity represented by an alias. In this section, we outline the methodology we used to label addresses and aliases. Our approach predominantly relied on off-chain data, distinguishing it fr… view at source ↗

**Figure 3.** Top: Frequency distribution of the number of messages per thread. Bottom: Frequency distribution of the number of posters per thread. 9/15 [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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