Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1075
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3195
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Modeling complex contagion in networked systems is an important topic in network science, for which various models have been proposed, including the synergistic contagion model that incorporates coherent interference and the simplicial contagion model that involves high-order interactions. Although both models have demonstrated success in investigating complex contagions, their relationship in modeling complex contagions remains unclear. In this study, we compare the synergy and the simplest form of high-order interaction in the simplicial contagion model, known as the triangular one. We analytically show that the triangular interaction and the synergy can be bridged within complex contagions through the joint degree distribution of the network. Monte Carlo simulations are then conducted to compare simplicial and corresponding synergistic contagions on synthetic and real-world networks, the results of which highlight the consistency of these two different contagion processes and thus validate our analysis. Our study sheds light on the deep relationship between the synergy and high-order interactions and enhances our physical understanding of complex contagions in networked systems.
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http://dx.doi.org/10.1063/5.0165572 | DOI Listing |