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Universal Patterns in the Blockchain: Analysis of EOAs and Smart Contracts in ERC20 Token Networks

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper claims that human and automated Ethereum accounts leave statistically distinguishable, time-persistent scaling signatures in ERC20 token transfers: human activity is near-universal, contract activity is contextual and volatile.

desk verdict Useful empirical split between human- and contract-driven ERC20 activity, but the abstract overstates the stability of EOA power-law exponents; worth peer review with revisions. read the letter →

arxiv 2508.04671 v1 pith:BNCUMWI6 submitted 2025-08-06 q-fin.ST cs.SIphysics.soc-ph

Kundan Mukhia , SR Luwang , Md. Nurujjaman , Tanujit Chakraborty , Suman Saha , Chittaranjan Hens This is my paper · ORCID
classification q-fin.STcs.SIphysics.soc-ph
keywords ERC20tokensEthereumscalinglawspower-lawdistributionsTaylor'slawsmartcontractsexternallyownedaccountsblockchaintransactionnetworks
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 examines 44.8 million ERC20 token transfers on Ethereum over nine months, split into three equal periods, and asks whether human-controlled accounts and automated smart contracts obey different statistical laws. Transfers are classified by whether the sender and receiver are externally owned accounts (EOAs) or smart contracts, giving four interaction types. For EOA-driven traffic, the authors find near-linear growth of trade volume with partner count ($\alpha \approx 1$), stable power-law exponents around $\gamma \approx 2.3$, and consistent Taylor scaling $\beta \approx 2.3$ across all three periods. For contract-to-contract traffic, scaling is sublinear ($\alpha$ falls from 0.93 to 0.67), power-law fits are unstable with heavier tails ($\gamma < 2$), and Taylor exponents fluctuate widely ($\Delta\beta = 0.51$). If correct, these signatures give a principled way to separate human from algorithmic behavior in decentralized finance, with human activity resembling universal scaling seen in social and economic systems.

What carries the argument

The argument is carried by a four-way transaction classification (EOA-to-EOA, EOA-to-contract, contract-to-EOA, contract-to-contract) built from the dataset's sender-contract and receiver-contract flags, combined with two canonical scaling tools: the volume-partner relation $V \sim N^\alpha$ and temporal Taylor's law $\sigma^2 = a\mu^\beta$, with power-law distributions $p(x) \propto x^{-\gamma}$ fitted by maximum likelihood and validated by Kolmogorov-Smirnov distances and log-likelihood ratios against an exponential. The four-way classification is the load-bearing object; the contrast between stable human-side exponents and drifting contract-side exponents is the evidence.

What would settle it

Take a random sample of addresses the dataset flags as externally owned, check the Ethereum chain state at those times to see whether each address actually has code, then re-fit $\alpha$, $\gamma$, and $\beta$ after correcting mislabeled cases. If corrected EOA-EOA exponents become unstable or corrected contract-contract exponents become near-linear, the reported contrast is a labeling artifact.

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Extended reading notes

Core claim

The central discovery is that ERC20 transaction statistics split cleanly along the EOA/smart-contract boundary. EOA-to-EOA and EOA-to-contract transactions show stable, near-universal exponents: volume grows nearly linearly with partner diversity, transaction counts follow power laws with $\gamma \approx 2.3$ whose thresholds are small and whose fits are good, and hourly variance obeys Taylor's law with $\beta \approx 2.3$ over every period. Smart-contract-to-contract transactions behave differently: volume growth is increasingly sublinear, power-law fits require very high thresholds and show larger errors and KS distances, and Taylor exponents wander, with the sender exponent dropping to $1

Load-bearing premise

The whole human-versus-algorithm contrast depends on the dataset's contract flags being correct; if addresses are mislabeled, the two statistical signatures blur together and the claimed universal split disappears.

Editorial extensions

If this is right

  • EOA-to-EOA and EOA-to-contract activity can be modeled as approximately universal: $\alpha \approx 1$, $\gamma \approx 2.3$ and $\beta \approx 2.3$ hold across nine months including a major market crash.
  • Contract-to-contract activity is not universal: its scaling exponents drift over time and its fits degrade, so decentralized-finance models need protocol-specific rather than one-size-fits-all assumptions.
  • The Taylor-exponent instability gap ($\Delta\beta = 0.51$ for contract-contract versus $0.17$ for EOA-EOA) provides a quantitative measure of how much more volatile automated flows are than human flows.
  • Heavy-tailed contract activity ($\gamma < 2$) means a small set of contracts dominates outgoing or incoming volume, so tail-risk monitoring in ERC20 networks should concentrate on contract-to-contract channels.

Reading between the lines

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

  • The paper never validates the contract flags it relies on; checking a sample of labeled addresses against on-chain bytecode would either confirm the human-versus-algorithm split or reveal it as a labeling artifact.
  • The near-quadratic Taylor exponents ($\beta \approx 2.3$) may partly reflect generic overdispersion in zero-inflated hourly counts rather than a specific behavioral mechanism; a zero-inflated or negative-binomial null model would show how much of the scaling is informative.
  • If the contrast replicates on other chains, the stable EOA fingerprint could serve as a baseline for anomaly detection in DeFi, flagging addresses whose scaling suddenly becomes contract-like as possible bots or wash traders.
  • The EOA-to-contract channel behaves like the human channel rather than the contract channel, suggesting that when a human initiates an interaction, the statistical signature remains human-like even though the counterparty is automated.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper analyzes 44.86 million ERC20 token transfers on Ethereum (July 2017–March 2018), splits them into three three-month periods, and classifies each transfer into EOA–EOA, EOA–SC, SC–EOA, or SC–SC using the fromIsContract/toIsContract flags of the XBlock-ETH dataset. For each class and period it estimates three scaling quantities: the volume–partner exponent α, the power-law tail exponent γ with KS-selected xmin, and the temporal Taylor's law exponent β from hourly mean–variance regressions. The paper's central claim is that EOA-driven transactions show stable, near-universal scaling (γ ≈ 2.3, β ≈ 2.3), while SC-involved transactions, especially SC–SC, show sublinear scaling, unstable exponents, and larger Taylor-exponent fluctuations (ΔβSC–SC = 0.51). The authors interpret this as a statistical distinction between human-controlled and algorithmic behavior in decentralized finance.

Significance. If the quantitative claim were fully supported, the paper would provide a useful, interpretable signature separating human and automated agents in blockchain transaction networks, and it would connect ERC20 activity to scaling laws observed in other social and economic systems. The study has clear strengths: it uses a public dataset, it reports detailed per-period tables (Tables 2, 3, and 5), and it combines two complementary scaling frameworks, power-law analysis and Taylor's law. The qualitative contrast — EOA-side exponents are generally higher and more stable, SC–SC exponents are lower and more variable — is visible in the paper's own tables. However, the specific headline claim of time-persistent 'stable γ ≈ 2.3' is contradicted by Table 3, and the statistical validation of the power-law and Taylor-law fits is incomplete in ways that affect the load-bearing conclusions.

major comments (4)
  1. [Abstract; Table 3] The abstract's first pillar — 'stable power law exponents (γ ≈ 2.3)' for EOA-driven transactions — is not supported by the paper's own Table 3. The purest EOA-side role, EOA–EOA sender, has γ = 2.32, 2.26, 1.76 across periods, with xmin jumping to 105 in Period 3; EOA–SC receiver falls from 2.07 to 1.89. These values put EOA-side activity into the γ < 2 regime that the paper elsewhere associates with SC-driven activity. The text's explanation that the 2018 crypto crash caused this is a one-off post hoc qualification; it does not rescue an abstract claim of universal, time-persistent behavior. The authors should either restrict the stability claim to Periods 1–2, model the crash period explicitly, or provide a statistical test showing that γ = 1.76 is consistent with γ ≈ 2.3 once xmin and sample-size effects are accounted for.
  2. [Power Law Distribution; Table 3] The power-law validation is incomplete. The paper reports KS distances and LLR values but no bootstrap goodness-of-fit p-values of the type recommended by Clauset et al. (2009), and it gives no confidence intervals for γ or xmin. The KS acceptance thresholds stated in 'Determining Threshold and Model Evaluation' (D < 0.05 good, D > 0.1 bad) are ad hoc, and xmin is estimated from the same data used to evaluate the fit. Consequently, the reader cannot tell whether the Period 3 EOA–EOA sender value γ = 1.76 is a genuine regime shift or an artifact of threshold selection in a thin tail. Please report full Clauset-style p-values, confidence intervals on γ, and a sensitivity analysis over xmin choices.
  3. [Stationarity Test Results; Temporal TL Exponent] The >90% KPSS stationarity percentages in Table 4 are not persuasive for hourly count series that are likely zero-inflated. A trader with many zero-activity hours and occasional nonzero hours will pass KPSS almost trivially, and regressing log-variance on log-mean for such sparse series can produce apparent β ≈ 2–2.5 without reflecting a stable fluctuation law. The paper should report the distribution of nonzero hours per trader, the number of traders contributing to each TL fit, and the raw mean–variance scatter with point counts. Without this, the claim that EOA-driven transactions 'adhere to TL with β ≈ 2.3' is not independently grounded.
  4. [Section 1.1; Table 1] The entire four-way classification relies on the fromIsContract and toIsContract flags in the XBlock-ETH dataset, yet the paper never validates this labeling. Misclassified early contracts, proxy contracts, or multi-sig wallets labeled as EOAs could contaminate the exact buckets on which the human-versus-algorithm contrast depends. Please validate the flags against an independent source (e.g., code existence at the address, contract-creation block, or Etherscan labels) or at least quantify the expected error rate and discuss how it would affect the contrasts in Tables 2, 3, and 5.
minor comments (5)
  1. [Introduction / organization] The last paragraph of the introduction says 'Section 1.1 presents and discusses the results' and 'Section 1.1 offers concluding remarks,' but the Results and Conclusion sections are not numbered subsections of Section 1.1. Please fix the section cross-references.
  2. [Table 5 and text near Figure 9] The text says SC–SC sender β drops to 1.96 'during Period 2' and recovers to 2.09 in Period 3, which matches Table 5; but the surrounding sentence for EOA–SC says the sender decreases in Period 3 while receiver increases. Please make the period labels consistent and avoid a possible reading that the SC–SC sender drop occurs in Period 3.
  3. [Figure 8 caption] The caption says 'Consistent β values (∼ 2) for senders across three periods,' but Table 5 lists EOA–EOA sender β ≈ 2.35. Please write β ≈ 2.3 or 'near-quadratic' rather than '∼2' if the caption is meant to be precise.
  4. [References] Reference [57] cites 'Is Newey–West optimal among first-order kernels?' by Kolokotrones, Stock, and Walker, but the Newey–West automatic bandwidth estimator is due to Newey and West. Please verify the citation and cite the original Newey–West papers.
  5. [Typos] Several table captions begin with 'T able' (Tables 1–5). Please remove the stray space throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: all reported exponents are empirical fits to external XBlock-ETH data; self-citations are background only.

full rationale

The paper's derivation chain is data-to-measurement. Transaction categories are defined by the fromIsContract/toIsContract flags in the external XBlock-ETH dataset (Table 1, Section 1.1). The scaling exponent alpha is estimated by least-squares regression of log V on log N (Eq. 2); the power-law gamma is estimated by MLE with xmin chosen by KS minimization (Eq. 3, Section 1.1); and the Taylor exponent beta is estimated by regression of log variance on log mean of hourly counts (Eq. 10). None of these quantities is defined in terms of another, and none is fitted to a subset and then 'predicted' for a closely related quantity. The cross-table consistency (alpha, gamma, beta 'confirming' each other) is a within-sample descriptive comparison, not a derivation or an out-of-sample prediction. Self-citations (e.g., Mukhia et al. 2024, Chakraborty et al. 2022/2025, Hens et al. 2019, Saha et al. 2020) are used as background/methodology references and are not load-bearing: no uniqueness theorem, ansatz, or central premise is imported from them. The caveats raised by the reader (unvalidated EOA/SC flags; zero-inflated hourly series making KPSS near-trivial; Period-3 gamma values in Table 3 contradicting the 'stable gamma=2.3' headline) are data-quality, statistical-validity, and internal-consistency concerns, not circularity. Accordingly, no circular step can be quoted, and the score is 0.

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

The paper introduces no invented entities. The free parameters are analysis choices (binning, windows, thresholds) and fitted cutoffs (xmin) that shape all reported exponents; the axioms are the contract classification, the stationarity/TL applicability, and the representativeness of the 9-month ICO-era window. The headline exponents themselves are fitted outputs reported as measurements; the ledger exposes the upstream choices that make them.

free parameters (5)
  • KS fit-quality thresholds = D below 0.05 'close fit', D above 0.1 'substantial deviation'
    Chosen by hand in 'Determining Threshold and Model Evaluation'; these thresholds decide which gamma fits are presented as evidence for power-law behavior, though the cited Clauset et al. protocol prescribes a bootstrap p-value instead of fixed KS cutoffs.
  • Log-bin count for volume-partner scaling = 20 bins
    Chosen by hand in the scaling-law section; the fitted alpha values in Table 2 are computed from log-binned averages, so alpha depends on this binning choice, and no sensitivity analysis is reported.
  • TL time window = 1 hour
    Chosen by hand in 'Temporal Taylor's Law'; all beta values in Table 5 are contingent on hourly binning, and no robustness check with other window sizes is reported.
  • xmin (power-law lower threshold) = e.g., 825 for SC-SC sender Period 3; 4 to 625 elsewhere
    Fitted per category and period by minimizing KS distance; for SC-SC Period 3 only the most active contracts remain, and gamma is then estimated on a heavily pruned sample with standard error 0.1148.
  • Period boundaries = three 3-month windows (Jul-Sep 2017, Oct-Dec 2017, Jan-Mar 2018)
    Chosen by hand; the 'stability across periods' claim is only as strong as this segmentation, which splits the sample right before and during the early-2018 crash.
assumptions (5)
  • domain assumption fromIsContract/toIsContract flags in XBlock-ETH correctly identify EOAs and smart contracts
    The entire four-way classification (Table 1) rests on this; never validated in the paper.
  • domain assumption Hourly transaction-count series that pass KPSS (p above 0.05) are suitable for Taylor's law fitting
    Table 4 reports over 90 percent stationary, but sparse mostly-zero series pass KPSS almost trivially; no check that variance-mean scaling is meaningful for zero-inflated counts.
  • ad hoc to paper Power-law model with KS-selected xmin, evaluated by in-sample KS distance and LLR versus exponential, is sufficient evidence of power-law behavior
    Clauset et al. 2009 (cited) prescribe a bootstrap goodness-of-fit p-value; the paper substitutes fixed KS cutoffs, making the 'consistently follow power-law behavior' claim weaker than stated.
  • domain assumption The period July 2017 to March 2018 is representative for inferring universal scaling in ERC20 networks
    A single early, ICO-dominated window; the conclusion generalizes to 'universal patterns' from it.
  • standard math MLE and KS methodology (Clauset et al.) is a valid estimation framework
    Standard background for power-law fitting; used throughout the analysis.

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Cite this review

Pith. "Pith review of Universal Patterns in the Blockchain: Analysis of EOAs and Smart Contracts in ERC20 Token Networks." pith.science (2026). https://pith.science/paper/BNCUMWI6

@misc{pith2026250804671,
  author       = {Pith},
  title        = {Pith review of: Universal Patterns in the Blockchain: Analysis of EOAs and Smart Contracts in ERC20 Token Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BNCUMWI6}},
  note         = {Machine review of arXiv:2508.04671}
}
abstract

Scaling laws offer a powerful lens to understand complex transactional behaviors in decentralized systems. This study reveals distinctive statistical signatures in the transactional dynamics of ERC20 tokens on the Ethereum blockchain by examining over 44 million token transfers between July 2017 and March 2018 (9-month period). Transactions are categorized into four types: EOA--EOA, EOA--SC, SC-EOA, and SC-SC based on whether the interacting addresses are Externally Owned Accounts (EOAs) or Smart Contracts (SCs), and analyzed across three equal periods (each of 3 months). To identify universal statistical patterns, we investigate the presence of two canonical scaling laws: power law distributions and temporal Taylor's law (TL). EOA-driven transactions exhibit consistent statistical behavior, including a near-linear relationship between trade volume and unique partners with stable power law exponents ($\gamma \approx 2.3$), and adherence to TL with scaling coefficients ($\beta \approx 2.3$). In contrast, interactions involving SCs, especially SC-SC, exhibit sublinear scaling, unstable power-law exponents, and significantly fluctuating Taylor coefficients (variation in $\beta$ to be $\Delta\beta = 0.51$). Moreover, SC-driven activity displays heavier-tailed distributions ($\gamma < 2$), indicating bursty and algorithm-driven activity. These findings reveal the characteristic differences between human-controlled and automated transaction behaviors in blockchain ecosystems. By uncovering universal scaling behaviors through the integration of complex systems theory and blockchain data analytics, this work provides a principled framework for understanding the underlying mechanisms of decentralized financial systems.

Figures

Figures reproduced from arXiv: 2508.04671 by the authors.

Figure 1
Figure 1. In Period 1, Scatter plots in log-log scale depict the relationship between the number of trades executed per unique trader and the number of unique trade partners. Each subplot corresponds to one transaction type: (a) EOA–EOA, (b) EOA–SC, (c) SC–EOA, and (d) SC-SC. Black dots represent individual traders, while brown curves show log-binned averages, highlighting the underlying scaling trend. These visualizations pr… view at source ↗
Figure 2
Figure 2. Period 2: Scatter plots in log-log scale showing the relationship between the number of trades executed per unique trader and the number of unique trade partners across four categories during the middle period of our observation time window. Black markers indicate individual trading accounts, while the brown curves show log-binned averages that highlight the overall scaling behavior. The consistent clustering of low… view at source ↗
Figure 3
Figure 3. Period 3: Scatter plots in log-log scale illustrating the relationship between the number of trades executed per unique trader and the number of unique trade partners across four categories. Black markers denote individual trading accounts, and the brown curves represent log-binned averages that reveal the overall scaling pattern. These plots help identify changes in interaction behavior in the final third of the ob… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Power-law fits of trading activities for EOA–EOA transactions across three time periods. The top row shows sender distributions and the bottom row shows receiver distributions, each on a log-log scale. Points represent log-binned empirical data, while the fitted lines …
Figure 5
Figure 5. Figure 5: Power-law fits of trading activity for EOA–SC transactions, where EOAs transfer tokens to SCs. The top row shows sender-side activity (EOAs) and the bottom row shows receiver-side activity (SC) across three time periods. Each subplot displays the empirical distribution…
Figure 6
Figure 6. Figure 6: Power-law fits of trading activity for SC–EOA transactions, where SCs send tokens to EOAs. The top row shows sender-side activity (SC), and the bottom row shows receiver-side activity (EOAs) across three distinct periods. Plotted on log-log scales, each subplot compare…
Figure 7
Figure 7. Figure 7: Power-law fits of trading activity for SC-SC transactions, where both sender and receiver are SC. The top row shows sender-side contract activity, and the bottom row shows receiver-side contract activity over three time periods. Plotted on log-log scales, these subfigu…
Figure 8
Figure 8. Figure 8: Scatter plot of log(mean) versus log(variance) for EOA–EOA transactions. The top and bottom rows illustrate, respectively, sender and receiver activities during three observation time windows. The slope of the linear fit represents the temporal TL exponent β, which cap…
Figure 9
Figure 9. Figure 9: Temporal TL exponents, derived for EOA–SC transactions, are shown across three periods. The top and bottom rows show sender activity (EOAs sending tokens to SC) and receiver activity (SC receiving tokens), respectively. Each point represents a trader. The linear fit of…
Figure 10
Figure 10. Figure 10: Temporal TL for SC–EOA transactions. The top row shows sender activity (SC sending tokens to EOAs), and the bottom row shows receiver activity (EOAs receiving tokens), across three periods. Each point corresponds to a trader, with log-mean and log-variance plotted. Th…
Figure 11
Figure 11. Figure 11: Temporal TL for SC–SC transactions. The top row shows sender-side activity (SC initiating transfers), while the bottom row shows receiver-side activity (SC receiving tokens) across three time periods. Each point plots the log-mean versus log-variance of hourly trade v…

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Reference graph

Works this paper leans on

62 extracted references · 62 canonical work pages · cited by 1 Pith paper

  1. [1]

    Bitcoin: A peer-to-peer electronic cash system

    Satoshi Nakamoto. “Bitcoin: A peer-to-peer electronic cash system”. In: (2008)

    work page 2008

  2. [2]

    Dynamic connectedness and integration in cryptocurrency mar- kets

    Qiang Ji et al. “Dynamic connectedness and integration in cryptocurrency mar- kets”. In: International Review of Financial Analysis 63 (2019), pp. 257–272

    work page 2019

  3. [3]

    Persistence in the cryptocurrency market

    Guglielmo Maria Caporale, Luis Gil-Alana, and Alex Plastun. “Persistence in the cryptocurrency market”. In: Research in International Business and Finance 46 (2018), pp. 141–148

    work page 2018

  4. [4]

    Cryptocurrency market structure: connecting emotions and eco- nomics

    Tomaso Aste. “Cryptocurrency market structure: connecting emotions and eco- nomics”. In: Digital Finance 1.1 (2019), pp. 5–21

    work page 2019

  5. [5]

    Complex network analysis of cryptocurrency market dur- ing crashes

    Kundan Mukhia et al. “Complex network analysis of cryptocurrency market dur- ing crashes”. In: Physica A: Statistical Mechanics and its Applications 653 (2024), p. 130095

    work page 2024

  6. [6]

    https://coinmarketcap.com/

    CoinMarketCap. https://coinmarketcap.com/. Accessed: April 28, 2025

    work page 2025

  7. [7]

    An empirical study of volatility in cryptocurrency market

    Hemendra Gupta and Rashmi Chaudhary. “An empirical study of volatility in cryptocurrency market”. In: Journal of Risk and Financial Management 15.11 (2022), p. 513

    work page 2022

  8. [8]

    Detecting and quantifying wash trading on decentralized cryptocurrency exchanges

    Friedhelm Victor and Andrea Marie Weintraud. “Detecting and quantifying wash trading on decentralized cryptocurrency exchanges”. In: Proceedings of the Web Conference 2021. 2021, pp. 23–32

    work page 2021

Show all 62 references
  1. [9]

    Blockchain challenges and opportunities: A survey

    Zibin Zheng et al. “Blockchain challenges and opportunities: A survey”. In: Inter- national journal of web and grid services 14.4 (2018), pp. 352–375

    2018

  2. [10]

    An overview of blockchain technology: Architecture, consensus, and future trends

    Zibin Zheng et al. “An overview of blockchain technology: Architecture, consensus, and future trends”. In: 2017 IEEE international congress on big data (BigData congress). Ieee. 2017, pp. 557–564

    2017

  3. [11]

    Cryptocurrency Market Overview

    Binance. Cryptocurrency Market Overview. Accessed: April 2025. 2025. url: https: //www.binance.com

    2025

  4. [12]

    Traveling the token world: A graph analysis of ethereum erc20 token ecosystem

    Weili Chen et al. “Traveling the token world: A graph analysis of ethereum erc20 token ecosystem”. In: Proceedings of The Web Conference 2020 . 2020, pp. 1411– 1421

    2020

  5. [13]

    Measuring ethereum-based erc20 token networks

    Friedhelm Victor and Bianca Katharina Lüders. “Measuring ethereum-based erc20 token networks”. In: Financial Cryptography and Data Security: 23rd Interna- tional Conference, FC 2019, Frigate Bay, St. Kitts and Nevis, February 18–22, 2019, Revised Selected Papers 23 . Springer....

    2019

  6. [14]

    A next-generation smart contract and decentralized appli- cation platform

    Vitalik Buterin et al. “A next-generation smart contract and decentralized appli- cation platform”. In: white paper 3.37 (2014), pp. 2–1

    2014

  7. [15]

    The idea of smart contracts

    Nick Szabo. “The idea of smart contracts”. In: Nick Szabos papers and concise tutorials 6.1 (1997), p. 199

    1997

  8. [16]

    Smart contracts

    Merit Kolvart, Margus Poola, and Addi Rull. “Smart contracts”. In: The Future of Law and etechnologies (2016), pp. 133–147

    2016

  9. [17]

    Impact of Black Swan Events on Ethereum blockchain ERC20 token transaction networks

    Moturi Pradeep et al. “Impact of Black Swan Events on Ethereum blockchain ERC20 token transaction networks”. In: Physica A: Statistical Mechanics and its Applications 654 (2024), p. 130129. August 6, 2025 22/25

    2024

  10. [18]

    Modeling innovation in the cryptocurrency ecosystem

    Giordano De Marzo, Francesco Pandolfelli, and Vito DP Servedio. “Modeling innovation in the cryptocurrency ecosystem”. In: Scientific Reports 12.1 (2022), p. 12942

    2022

  11. [19]

    Network analysis of erc20 tokens trading on ethereum blockchain

    Shahar Somin, Goren Gordon, and Yaniv Altshuler. “Network analysis of erc20 tokens trading on ethereum blockchain”. In: Unifying Themes in Complex Systems IX: Proceedings of the Ninth International Conference on Complex Systems 9 . Springer. 2018, pp. 439–450

    2018

  12. [20]

    Classification of cryptocurrency coins and tokens by the dynamics of their market capitalizations

    Ke Wu, Spencer Wheatley, and Didier Sornette. “Classification of cryptocurrency coins and tokens by the dynamics of their market capitalizations”. In: Royal So- ciety open science 5.9 (2018), p. 180381

    2018

  13. [21]

    Searching for heavy-tailed probability distributions for modeling real-world complex networks

    Tanujit Chakraborty et al. “Searching for heavy-tailed probability distributions for modeling real-world complex networks”. In: IEEE Access 10 (2022), pp. 115092– 115107

    2022

  14. [22]

    A Compounded Burr Probability Distribution for Fitting Heavy-Tailed Data with Applications to Biological Networks

    Tanujit Chakraborty et al. “A Compounded Burr Probability Distribution for Fitting Heavy-Tailed Data with Applications to Biological Networks”. In: Chaos (2025)

    2025

  15. [23]

    Aggregation, Variance and the Mean

    L. R. Taylor. “Aggregation, Variance and the Mean”. In: Nature 189.4766 (1961), pp. 732–735. doi: 10 . 1038 / 189732a0. url: https : / / doi . org / 10 . 1038 / 189732a0

    1961

  16. [24]

    Fluctuation scaling in complex systems: Taylor’s law and beyond

    Zoltán Eisler, Imre Bartos, and János Kertész. “Fluctuation scaling in complex systems: Taylor’s law and beyond”. In: Advances in Physics 57.1 (2008), pp. 89– 142

    2008

  17. [25]

    Taylors law of temporal fluctua- tion scaling in stock illiquidity

    Qing Cai, Hai-Chuan Xu, and Wei-Xing Zhou. “Taylors law of temporal fluctua- tion scaling in stock illiquidity”. In: Fluctuation and Noise Letters 15.04 (2016), p. 1650029

    2016

  18. [26]

    Xblock-eth: Extracting and exploring blockchain data from ethereum

    Peilin Zheng et al. “Xblock-eth: Extracting and exploring blockchain data from ethereum”. In: IEEE Open Journal of the Computer Society 1 (2020), pp. 95–106

    2020

  19. [27]

    http://xblock.pro/xblock-eth.html

    XBlock-ETH Dataset Website . http://xblock.pro/xblock-eth.html

  20. [28]

    https://en.wikipedia.org/wiki/Unix_time

    Unix time . https://en.wikipedia.org/wiki/Unix_time

  21. [29]

    Growth, innovation, scaling, and the pace of life in cities

    Luís MA Bettencourt et al. “Growth, innovation, scaling, and the pace of life in cities”. In: Proceedings of the national academy of sciences 104.17 (2007), pp. 7301–7306

    2007

  22. [30]

    The origins of scaling in cities

    Luís MA Bettencourt. “The origins of scaling in cities”. In: science 340.6139 (2013), pp. 1438–1441

    2013

  23. [31]

    The scaling of human interactions with city size

    Markus Schläpfer et al. “The scaling of human interactions with city size”. In: Journal of the Royal Society Interface 11.98 (2014), p. 20130789

    2014

  24. [32]

    Spatiotemporal signal propagation in complex net- works

    Chittaranjan Hens et al. “Spatiotemporal signal propagation in complex net- works”. In: Nature Physics 15.4 (2019), pp. 403–412

    2019

  25. [33]

    Power-law distri- butions in empirical data

    Aaron Clauset, Cosma Rohilla Shalizi, and Mark EJ Newman. “Power-law distri- butions in empirical data”. In: SIAM review 51.4 (2009), pp. 661–703

    2009

  26. [34]

    A brief history of generative models for power law and lognormal distributions

    Michael Mitzenmacher. “A brief history of generative models for power law and lognormal distributions”. In: Internet mathematics 1.2 (2004), pp. 226–251

    2004

  27. [35]

    Emergence of scaling in random net- works

    Albert-László Barabási and Réka Albert. “Emergence of scaling in random net- works”. In: science 286.5439 (1999), pp. 509–512

    1999

  28. [36]

    Power laws, Pareto distributions and Zipf’s law

    Mark EJ Newman. “Power laws, Pareto distributions and Zipf’s law”. In: Con- temporary physics 46.5 (2005), pp. 323–351. August 6, 2025 23/25

    2005

  29. [37]

    The powerful law of the power law and other myths in network biology

    Gipsi Lima-Mendez and Jacques Van Helden. “The powerful law of the power law and other myths in network biology”. In: Molecular BioSystems 5.12 (2009), pp. 1482–1493

    2009

  30. [38]

    Infection spreading and recovery in a square lattice

    Suman Saha et al. “Infection spreading and recovery in a square lattice”. In: Physical Review E 102.5 (2020), p. 052307

    2020

  31. [39]

    Prediction and asymptotics

    Ole E Barndorff-Nielsen and David R Cox. “Prediction and asymptotics”. In: (1996)

    1996

  32. [40]

    All of statistics: a concise course in statistical inference

    Larry Wasserman. All of statistics: a concise course in statistical inference . Springer Science & Business Media, 2013

    2013

  33. [41]

    Problems with fitting to the power-law distribution

    Michel L Goldstein, Steven A Morris, and Gary G Yen. “Problems with fitting to the power-law distribution”. In: The European Physical Journal B-Condensed Matter and Complex Systems 41 (2004), pp. 255–258

    2004

  34. [42]

    Kolmogorov–smirnov test: Overview

    Vance W Berger and YanYan Zhou. “Kolmogorov–smirnov test: Overview”. In: Wiley statsref: Statistics reference online (2014)

    2014

  35. [43]

    On the frequency of severe terrorist events

    Aaron Clauset, Maxwell Young, and Kristian Skrede Gleditsch. “On the frequency of severe terrorist events”. In: Journal of Conflict Resolution 51.1 (2007), pp. 58– 87

    2007

  36. [44]

    The Kolmogorov-Smirnov test for goodness of fit

    Frank J Massey Jr. “The Kolmogorov-Smirnov test for goodness of fit”. In: Journal of the American statistical Association 46.253 (1951), pp. 68–78

    1951

  37. [45]

    Using likelihood to test for Lévy flight search patterns and for general power-law distributions in nature

    Andrew M Edwards. “Using likelihood to test for Lévy flight search patterns and for general power-law distributions in nature”. In: Journal of Animal Ecology 77.6 (2008), pp. 1212–1222

    2008

  38. [46]

    Statistical analyses support power law distributions found in neuronal avalanches

    Andreas Klaus, Shan Yu, and Dietmar Plenz. “Statistical analyses support power law distributions found in neuronal avalanches”. In: PloS one 6.5 (2011), e19779

    2011

  39. [47]

    Response surface estimates of the KPSS stationarity test

    Peter S Sephton. “Response surface estimates of the KPSS stationarity test”. In: Economics Letters 47.3-4 (1995), pp. 255–261

    1995

  40. [48]

    Generalizations of the KPSS-test for stationarity

    Bart Hobijn, Philip Hans Franses, and Marius Ooms. “Generalizations of the KPSS-test for stationarity”. In: Statistica Neerlandica 58.4 (2004), pp. 483–502

    2004

  41. [49]

    Lag order and critical values of the augmented Dickey–Fuller test

    Yin-Wong Cheung and Kon S Lai. “Lag order and critical values of the augmented Dickey–Fuller test”. In: Journal of Business & Economic Statistics 13.3 (1995), pp. 277–280

    1995

  42. [50]

    Distribution of the estimators for au- toregressive time series with a unit root

    David A Dickey and Wayne A Fuller. “Distribution of the estimators for au- toregressive time series with a unit root”. In: Journal of the American statistical association 74.366a (1979), pp. 427–431

    1979

  43. [51]

    Testing for a unit root in time series re- gression

    Peter CB Phillips and Pierre Perron. “Testing for a unit root in time series re- gression”. In: biometrika 75.2 (1988), pp. 335–346

    1988

  44. [52]

    Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis

    Eric Zivot and Donald W K Andrews. “Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis”. In: Journal of business & economic statistics 20.1 (2002), pp. 25–44

    2002

  45. [53]

    Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?

    Denis Kwiatkowski et al. “Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?” In: Journal of econometrics 54.1-3 (1992), pp. 159–178

    1992

  46. [54]

    The KPSS stationarity test as a unit root test

    Yongcheol Shin and Peter Schmidt. “The KPSS stationarity test as a unit root test”. In: Economics Letters 38.4 (1992), pp. 387–392

    1992

  47. [55]

    Consistency of the KPSS unit root test against fractionally integrated alternative

    Hyung S Lee and Christine Amsler. “Consistency of the KPSS unit root test against fractionally integrated alternative”. In: Economics letters 55.2 (1997), pp. 151–160. August 6, 2025 24/25

    1997

  48. [56]

    Stationarity tests for financial time series

    Ricardo Gimeno, Benjamn Manchado, and Román Mnguez. “Stationarity tests for financial time series”. In: Physica A: Statistical Mechanics and its Applications 269.1 (1999), pp. 72–78

    1999

  49. [57]

    Is Newey– West optimal among first-order kernels?

    Thomas Kolokotrones, James H Stock, and Christopher D Walker. “Is Newey– West optimal among first-order kernels?” In: Journal of Econometrics 240.2 (2024), p. 105399

    2024

  50. [58]

    Random sampling of skewed distributions implies Taylors power law of fluctuation scaling

    Joel E Cohen and Meng Xu. “Random sampling of skewed distributions implies Taylors power law of fluctuation scaling”. In: Proceedings of the National Academy of Sciences 112.25 (2015), pp. 7749–7754

    2015

  51. [59]

    Modeling distances between humans using Taylors law and geometric probability

    Joel E Cohen and Daniel Courgeau. “Modeling distances between humans using Taylors law and geometric probability”. In: Mathematical Population Studies 24.4 (2017), pp. 197–218

    2017

  52. [60]

    Heavy-tailed dis- tributions, correlations, kurtosis and Taylors law of fluctuation scaling

    Joel E Cohen, Richard A Davis, and Gennady Samorodnitsky. “Heavy-tailed dis- tributions, correlations, kurtosis and Taylors law of fluctuation scaling”. In: Pro- ceedings of the Royal Society A 476.2244 (2020), p. 20200610

    2020

  53. [61]

    Origins of Taylors power law for fluctuation scaling in complex systems

    Agata Fronczak and Piotr Fronczak. “Origins of Taylors power law for fluctuation scaling in complex systems”. In: Physical Review EStatistical, Nonlinear, and Soft Matter Physics 81.6 (2010), p. 066112

    2010

  54. [62]

    The great crypto crash in September 2018: why did the cryp- tocurrency market collapse?

    Viktor Manahov. “The great crypto crash in September 2018: why did the cryp- tocurrency market collapse?” In: Annals of Operations Research 332.1 (2024), pp. 579–616. August 6, 2025 25/25

    2018

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.