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oai:figshare.com:article/34049170 2026-10-01T17:43:14Z category_4 category_7 category_12 category_13 category_16 category_272 category_39 category_45 category_46 portal_5 item_type_1 month_year_10_2026

<p>Temporal TL exponents, derived for EOA–SC transactions, are shown across three periods.</p> Kundan Mukhia (25157917) S. R. Luwang (25157920) Md. Nurujjaman (25157923) Tanujit Chakraborty (25157926) Suman Saha (390518) Chittaranjan Hens (8946065) Biochemistry Medicine Cell Biology Genetics Physiology Environmental Sciences not elsewhere classified Ecology Sociology Immunology uncovering scaling behaviors temporal taylor ’ scaling laws offer scaling coefficients (). greater computational complexity externally owned accounts exhibit sublinear scaling interactions involving scs complex systems theory smart contract execution blockchain data analytics gas fee analysis div >< p 3 months ). law exponents (), eoa – eoa eoa – sc scs ), law exponents decentralized systems smart contracts ethereum blockchain blockchain ecosystems work provides unique partners underlying mechanisms transactional dynamics trade volume structural differences statistical patterns principled framework powerful lens march 2018 linear relationship level activity law distributions july 2017 interacting addresses four types findings reveal february 2020 erc20 tokens december 2019 characteristic differences <p>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 the scatter plot between log-mean versus log-variance of hourly trade activity represents the TTL exponent . Consistent values for senders across three periods, suggesting predictable EOA-based trading behavior over time.</p> 2026-10-01T17:42:56Z Image Figure 10.1371/journal.pone.0358972.g009 https://figshare.com/articles/figure/_p_Temporal_TL_exponents_derived_for_EOA_SC_transactions_are_shown_across_three_periods_p_/34049170 CC BY 4.0