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Edge Proposal Sets for Link Prediction [article]

Abhay Singh, Qian Huang, Sijia Linda Huang, Omkar Bhalerao, Horace He, Ser-Nam Lim, Austin R. Benson
<span title="2021-06-30">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The underlying idea is that if the edges in the proposal set generally align with the structure of the graph, link prediction algorithms are further guided towards predicting the right edges; in other  ...  Here, we demonstrate how simply adding a set of edges, which we call a proposal set, to the graph as a pre-processing step can improve the performance of several link prediction algorithms.  ...  We use link prediction algorithms for both generating the proposal set from the starting set (filtering step) and for the final link predictions (ranking step).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.15810v1">arXiv:2106.15810v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/knaxp2o3yfagppej7w7hqdrkdu">fatcat:knaxp2o3yfagppej7w7hqdrkdu</a> </span>
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Evaluating Link Prediction Accuracy on Dynamic Networks with Added and Removed Edges [article]

Ruthwik R. Junuthula, Kevin S. Xu, Vijay K. Devabhaktuni
<span title="2016-07-25">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Many link prediction methods have been proposed, ranging from common neighbors to probabilistic models.  ...  In dynamic networks where edges are both added and removed over time, the link prediction problem is more complex and involves predicting both newly added and newly removed edges.  ...  Many link prediction methods have been proposed; see [2] , [3] for surveys of the literature.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1607.07330v1">arXiv:1607.07330v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k726tp47dvaqviefbl6lzozjsy">fatcat:k726tp47dvaqviefbl6lzozjsy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930041100/https://arxiv.org/pdf/1607.07330v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/97/36/9736feb5b4d5e84f7b2a9504344a0a0b39524f3c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1607.07330v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

An efficient method for link prediction in weighted multiplex networks

Shikhar Sharma, Anurag Singh
<span title="2016-11-05">2016</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/57o5y4hm2rgexgjtw745w52asu" style="color: black;">Computational Social Networks</a> </i> &nbsp;
This work further proposes and testifies a strategy for weight prediction.  ...  Results and Conclusions: This work successfully proposes an algorithm for Weight Prediction using Link similarity measures on multiplex networks.  ...  Let be a subset of E which represents the set of edges which are used for testing the link prediction algorithm and are removed from the original graph.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40649-016-0034-y">doi:10.1186/s40649-016-0034-y</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29355190">pmid:29355190</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5748725/">pmcid:PMC5748725</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f2cqnz357jeologfkb23g7gp74">fatcat:f2cqnz357jeologfkb23g7gp74</a> </span>
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A Weighted path based Link Prediction in Social Networks using Bounded Length of Separation between Nodes

Srilatha P, Manjula R
<span title="2018-10-02">2018</span> <i title="Science Publishing Corporation"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/piy2nrvrjrfcfoz5nmre6zwa4i" style="color: black;">International Journal of Engineering &amp; Technology</a> </i> &nbsp;
As a result, the proposed algorithm will be able to predict accurately than the existing link prediction algorithms.  ...  The classical methods of link prediction based on the topological structure of the graph exploit all different paths of the network which are being computationally expensive for large size of networks.  ...  Section 3 reviews the relevant methods in the area of link prediction in social networks. Section 4 proposes the new algorithm for finding the likelihood scores of all the non existing edges.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14419/ijet.v7i4.10.20911">doi:10.14419/ijet.v7i4.10.20911</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n75727srm5aubkvlbbtlxavxbq">fatcat:n75727srm5aubkvlbbtlxavxbq</a> </span>
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Proposing Ties in a Dense Hypergraph of Academics [chapter]

Aaron Gerow, Bowen Lou, Eamon Duede, James Evans
<span title="">2015</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Similar link prediction tasks have been primarily explored in unipartite settings, but for hypergraphs where hyper-edges out-number nodes 25-to-1, accounting for link similarity is crucial.  ...  The model is also compared to other link prediction methods in a static setting.  ...  Thanks to the SWIFT team (swiftlang.org) for help parallelizing various aspects of the model and to the Open Computing Consortium for computing resources.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-27433-1_15">doi:10.1007/978-3-319-27433-1_15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s4lzntsqhbbgpf3ugelrg5mwxe">fatcat:s4lzntsqhbbgpf3ugelrg5mwxe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180720204230/http://research.gold.ac.uk/22717/1/gerow_hypergraphs.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/59/14/5914d4eeb14fa12249efe1c5d7f70899bc0ff032.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-27433-1_15"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A Method for Improving the Accuracy of Link Prediction Algorithms

Jie Li, Xiyang Peng, Jian Wang, Na Zhao, Anirban Chakraborti
<span title="2021-05-22">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y3fh56bfunh5fgneywwba6d4ke" style="color: black;">Complexity</a> </i> &nbsp;
This study proposes a method for improving the accuracy of link prediction.  ...  Link prediction is a key tool for studying the structure and evolution mechanism of complex networks.  ...  After calculating the link score between every two nodes in the network through the training set, each time of an edge is randomly selected from the test and nonexistent edge sets for comparison.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/8889441">doi:10.1155/2021/8889441</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v3ibalvtsbeb5esw7xavroxrqa">fatcat:v3ibalvtsbeb5esw7xavroxrqa</a> </span>
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Link prediction for interdisciplinary collaboration via co-authorship network [article]

Haeran Cho, Yi Yu
<span title="2018-03-16">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Using the existing co-authorship network and academic information thereof, we propose a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in  ...  We analyse the Publication and Research (PURE) data set of University of Bristol collected between 2008 and 2013.  ...  Acknowledgements We thank the PURE team and the Jean Golding Institute at the University of Bristol for providing the data set. We thank Professor Jonathan C.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.06249v1">arXiv:1803.06249v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vkesm3brfbez3ftpa2zs4o3f74">fatcat:vkesm3brfbez3ftpa2zs4o3f74</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191016200256/https://arxiv.org/pdf/1803.06249v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a3/f3/a3f3a25a4864c5ac31ad99250d65c8b9aed3ed0a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.06249v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Towards Generating Explanations for ASP-Based Link Analysis using Declarative Program Transformations [article]

Martin Atzmueller and Cicek Güven and Dietmar Seipel
<span title="2019-09-08">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Applying ASP for link prediction provides a powerful declarative approach, e.g., for incorporating domain knowledge for explicative prediction.  ...  This paper investigates the problem of link analysis, specifically link prediction and anomalous link discovery in social networks using the declarative method of Answer set programming (ASP).  ...  We have exemplified the application and efficacy of the proposed approach in the context of link analysis, i. e., link prediction and anomalous link discovery for social networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.03404v1">arXiv:1909.03404v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yvqttc5oanbvfes7l3iu6zscwu">fatcat:yvqttc5oanbvfes7l3iu6zscwu</a> </span>
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Link prediction for interdisciplinary collaboration via co-authorship network

Haeran Cho, Yi Yu
<span title="2018-03-27">2018</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3xvqvdkqejfvdeozx2l3c3rxti" style="color: black;">Social Network Analysis and Mining</a> </i> &nbsp;
Using the existing co-authorship network and academic information thereof, we propose a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in  ...  We analyse the Publication and Research data set of University of Bristol collected between 2008 and 2013.  ...  Acknowledgements We thank the PURE team and the Jean Golding Institute at the University of Bristol for providing the data set. We thank Professor Jonathan C.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s13278-018-0501-6">doi:10.1007/s13278-018-0501-6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jlygaaeoyra7nmgu474cax5gyi">fatcat:jlygaaeoyra7nmgu474cax5gyi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20181030083348/https://link.springer.com/content/pdf/10.1007%2Fs13278-018-0501-6.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/df/cf/dfcf28fb0a2d78599f6c3bf6048f013dfa22427a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s13278-018-0501-6"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Link Prediction with Contextualized Self-Supervision [article]

Daokun Zhang, Jie Yin, Philip S. Yu
<span title="2022-01-25">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To overcome these challenges, we propose a Contextualized Self-Supervised Learning (CSSL) framework that fully exploits structural context prediction for link prediction.  ...  To generate node embeddings tailored for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost link prediction.  ...  Here, for the edge set E, we have E = E tr ∪ E te , where E tr is the set of training links and E te is the set of test links.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.10069v1">arXiv:2201.10069v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kehwqkrqfvgdpgiimxa3gk5ali">fatcat:kehwqkrqfvgdpgiimxa3gk5ali</a> </span>
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NENET: An Edge Learnable Network for Link Prediction in Scene Text [article]

Mayank Kumar Singh, Sayan Banerjee, Shubhasis Chaudhuri
<span title="2020-05-25">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The main advantage of using GNN for link prediction lies in its ability to connect characters which are spatially separated and have an arbitrary orientation.  ...  This necessitates the need to link adjacent characters, which we propose in this paper using a novel Graph Neural Network (GNN) architecture that allows us to learn both node and edge features as opposed  ...  NENET for Edge Prediction Let a set of characters obtained using the proposed character detection method from a predicted heat map O corresponding to a scene I be denoted by X = {x 1 , x 2 , ..., x n }  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.12147v1">arXiv:2005.12147v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aowflbv6wbaurosdmnaskzpft4">fatcat:aowflbv6wbaurosdmnaskzpft4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930145010/https://arxiv.org/pdf/2005.12147v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5c/aa/5caa0cb705682410b25e51739efd023b6db9efb7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.12147v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Identifying Negative Interactions in Protein-Protein Interaction Network Using Weak Edge-edge Domination Set

Sminu Izudheen, Sheena Mathew
<span title="">2016</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kcayf4mk7zbrbhvdwizgrxaruu" style="color: black;">Procedia Technology - Elsevier</a> </i> &nbsp;
In this paper, we propose a method to optimize the negative link predicted in protein network through Weak Edge-Edge Domination (WEED) set.  ...  But most of the work in this area was concentrated on predicting existence of links in future. Very few works has explored the prediction of links that might disappear in future.  ...  We then optimize the links predicted by calculating the Weak Edge-Edge Domination (WEED) set of the predicted links.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.protcy.2016.05.167">doi:10.1016/j.protcy.2016.05.167</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cfxpnmj6z5dqtlhpmcsl5zsej4">fatcat:cfxpnmj6z5dqtlhpmcsl5zsej4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171004091719/http://publisher-connector.core.ac.uk/resourcesync/data/elsevier/pdf/dcb/aHR0cDovL2FwaS5lbHNldmllci5jb20vY29udGVudC9hcnRpY2xlL3BpaS9zMjIxMjAxNzMxNjMwMjU2MA%3D%3D.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ac/11/ac113a024812d9f4b25fbb51a3cad69185620a3b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.protcy.2016.05.167"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Network Adjacency Matrix Blocked-compressive Sensing: A Novel Algorithm for Link Prediction

Fei Cai, Xiaohui Mou, Xin Zhang, Jie Chen, Jin Li, Wenpeng Xu
<span title="2019-04-20">2019</span> <i title="International Information and Engineering Technology Association"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/quh3djrbmrd7pnzo2z2z6e5cpa" style="color: black;">Ingénierie des Systèmes d&#39;Information</a> </i> &nbsp;
Link prediction for complex networks is a research hotspot. The main purpose is to predict the unknown edge according to the structure of the existing network.  ...  Considering the weak performance of traditional link prediction algorithms under the above situation, this paper puts forward a novel link prediction algorithm called network adjacency matrix blocked-compressive  ...  This poses a huge challenge to the existing link prediction methods, calling for the improving the link prediction in sparse network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18280/isi.240104">doi:10.18280/isi.240104</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/khb57reqqzhgrdnijoiqrywkjy">fatcat:khb57reqqzhgrdnijoiqrywkjy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200307130424/http://www.iieta.org/download/file/fid/696" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/83/ef/83ef88f9d459fe501144333e6f1134a3e30cc837.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18280/isi.240104"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Preparation of Papers for IEEE ACCESS

Zeguang Liu, Yao Li, Huilin Liu
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
The proposed link prediction algorithm uses the common influence set of two unconnected nodes to calculate a similarity score between the two nodes.  ...  In order to predict future node similarity, we propose a new model, Common Influence Set, to calculate node similarities.  ...  In this paper, we proposed a new similarity index and provide an effective method to calculate the top-k links for the link prediction problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2942357">doi:10.1109/access.2019.2942357</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dig34bwigfbh7isjkgzqgbp6ly">fatcat:dig34bwigfbh7isjkgzqgbp6ly</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717122446/https://ieeexplore.ieee.org/ielx7/6287639/8600701/08844661.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fa/d9/fad92a6868074edf8690aec08198a5a84d832557.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2942357"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

A Scalable Similarity-Popularity Link Prediction Method

Said Kerrache, Ruwayda Alharbi, Hafida Benhidour
<span title="2020-04-14">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a> </i> &nbsp;
Link prediction is the task of computing the likelihood that a link exists between two given nodes in a network.  ...  The aim of this work is to develop a scalable link prediction algorithm that offers a higher overall predictive power than existing methods.  ...  Part of the computational experiments reported in this work were conducted on the SANAM supercomputer at King Abdulaziz City for Science and Technology (hpc.kacst.edu.sa).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-020-62636-1">doi:10.1038/s41598-020-62636-1</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32286363">pmid:32286363</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7156691/">pmcid:PMC7156691</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pst4etgf7rfgdoxhtia2263wcm">fatcat:pst4etgf7rfgdoxhtia2263wcm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200505013947/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7156691&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d9/eb/d9eb83d984f1dae77323711d0099304cdec7b401.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-020-62636-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156691" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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