{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T09:01:16Z","timestamp":1773910876572,"version":"3.50.1"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T00:00:00Z","timestamp":1708992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"The Department of Science and Technology, Government of India","award":["DST-1401-CSE"],"award-info":[{"award-number":["DST-1401-CSE"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\n            Network modeling has been explored extensively by means of theoretical analysis as well as numerical simulations for Network Reconstruction (NR). The network reconstruction problem requires the estimation of the power-law exponent (\u03b3) of a given input network. Thus, the effectiveness of the NR solution depends on the accuracy of the calculation of \u03b3. In this article, we re-examine the degree distribution-based estimation of \u03b3, which is not very accurate due to approximations. We propose\n            <jats:bold>X<\/jats:bold>\n            -distribution, which is more accurate than degree distribution. Various state-of-the-art network models, including CPM, NRM, RefOrCite2, BA, CDPAM, and DMS, are considered for simulation purposes, and simulated results support the proposed claim. Further, we apply\n            <jats:bold>X<\/jats:bold>\n            -distribution over several real-world networks to calculate their power-law exponents, which differ from those calculated using respective degree distributions. It is observed that\n            <jats:bold>X<\/jats:bold>\n            -distributions exhibit more linearity (straight line) on the log-log scale than degree distributions. Thus,\n            <jats:bold>X<\/jats:bold>\n            -distribution is more suitable for the evaluation of power-law exponent using linear fitting (on the log-log scale). The MATLAB implementation of power-law exponent (\u03b3) calculation using\n            <jats:bold>X<\/jats:bold>\n            -distribution for different network models and the real-world datasets used in our experiments are available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Aikta-Arya\/X-distribution-Retraceable-Power-Law-Exponent-of-Complex-Networks.git\">https:\/\/github.com\/Aikta-Arya\/X-distribution-Retraceable-Power-Law-Exponent-of-Complex-Networks.git<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3639413","type":"journal-article","created":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T15:57:21Z","timestamp":1703951841000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["X-distribution: Retraceable Power-law Exponent of Complex Networks"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2601-7850","authenticated-orcid":false,"given":"Pradumn Kumar","family":"Pandey","sequence":"first","affiliation":[{"name":"Indian Institute of Technology, Roorkee, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0650-6611","authenticated-orcid":false,"given":"Aikta","family":"Arya","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Roorkee, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7151-6309","authenticated-orcid":false,"given":"Akrati","family":"Saxena","sequence":"additional","affiliation":[{"name":"LIACS, Leiden University, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","unstructured":"A. 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