{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:49:57Z","timestamp":1776181797747,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,15]],"date-time":"2017-11-15T00:00:00Z","timestamp":1510704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be \u201cvital\u201d to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary\u2019s karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/e19110614","type":"journal-article","created":{"date-parts":[[2017,11,15]],"date-time":"2017-11-15T11:13:35Z","timestamp":1510744415000},"page":"614","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach"],"prefix":"10.3390","volume":"19","author":[{"given":"Tong","family":"Qiao","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beihang University, Beijing 100191, China"}]},{"given":"Wei","family":"Shan","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Complex System Analysis and Management Decision, Ministry of Education, Beijing 100191, China"}]},{"given":"Chang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/0378-8733(78)90021-7","article-title":"Centrality in networks: I. 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