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Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics [article]

Anura P. Jayasumana, Randy Paffenroth, Gunjan Mahindre, Sridhar Ramasamy, Kelum Gajamannage
<span title="2018-09-07">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In particular, our approach is a combination of shortest path (often called geodesic) recovery concepts and low-rank matrix completion, generalized to the case of hop-distances in graphs.  ...  Herein, we present an approach to recover geometric and topological properties of a network with a small set of distance measurements.  ...  The main contribution of this paper is a technique that combines VC based techniques with low-rank matrix completion, that allows the extraction of topology and geometric features of a network from a small  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1809.03319v1">arXiv:1809.03319v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5aqx3cxhh5cq5jhwfkfc577hhm">fatcat:5aqx3cxhh5cq5jhwfkfc577hhm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200826113113/https://arxiv.org/pdf/1809.03319v1.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/09/85/09859abba1ad4810ce31043af787175f05ac3cd9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1809.03319v1" 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>

Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics [article]

Anura P. Jayasumana and Randy Paffenroth and Sridhar Ramasamy
<span title="2018-09-13">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Herein, we present an approach, based on the theory of low-rank matrix completion, to recover geometric properties of a network with only partial information about the VCs of nodes.  ...  In particular, our approach is a combination of geodesic recovery concepts and low-rank matrix completion, generalized to the case of hop-distances in graphs.  ...  Our approach combines network path sampling with matrix completion, and it is motivated by two observations.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.10063v2">arXiv:1712.10063v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wmw2nmkavnhczjr62eed2g3yha">fatcat:wmw2nmkavnhczjr62eed2g3yha</a> </span>
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Low rank modeling of signed networks

Cho-Jui Hsieh, Kai-Yang Chiang, Inderjit S. Dhillon
<span title="">2012</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD &#39;12</a> </i> &nbsp;
Under such a model, the sign inference problem can be formulated as a low-rank matrix completion problem.  ...  Most state-of-the-art approaches consider the notion of structural balance in signed networks, building inference algorithms based on information about links, triads, and cycles in the network.  ...  Theorem 3 (Low Rank Structure of Signed Networks) The adjacency matrix A of a complete k-weakly balanced network has rank 1 if k ≤ 2, and has rank k for all k > 2. Proof.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2339530.2339612">doi:10.1145/2339530.2339612</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/kdd/HsiehCD12.html">dblp:conf/kdd/HsiehCD12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h4gj2h4c7fdexnnmly6yfukyxm">fatcat:h4gj2h4c7fdexnnmly6yfukyxm</a> </span>
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Prediction and Clustering in Signed Networks: A Local to Global Perspective [article]

Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Ambuj Tewari and Inderjit S. Dhillon
<span title="2013-03-05">2013</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
For the low rank modeling approach, we provide theoretical performance guarantees via convex relaxations, scale it up to large problem sizes using a matrix factorization based algorithm, and provide extensive  ...  Furthermore, motivated by the global structure of balanced networks, we propose an effective low rank modeling approach for both sign prediction and clustering.  ...  We choose LR-SVP, LR-ALS, MOI-∞ and HOC-3 as representatives of the two approaches of low rank matrix completion, MOI-based, and HOC-based methods respectively.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1302.5145v2">arXiv:1302.5145v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wzbhqbrz7jhtdg5li7hnndnrye">fatcat:wzbhqbrz7jhtdg5li7hnndnrye</a> </span>
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Table of Contents

<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gkn2pu46ozb4tmkxczacnmtvkq" style="color: black;">IEEE Transactions on Signal Processing</a> </i> &nbsp;
Zeng, and M. A. Govoni Robust Low-Rank Tensor Completion Based on Tensor Ring Rank via p, -Norm . . . . . . . . . . . . . . . . X. P. Li and H. C.  ...  A. Richards, S. Bagchi, and S. Sundaram Quantization Games on Social Networks and Language Evolution . . . . . . . . . . . . A. Mani, L. R. Varshney, and A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tsp.2021.3136798">doi:10.1109/tsp.2021.3136798</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kzkdhzcz3fgx3jv6gfjofooseq">fatcat:kzkdhzcz3fgx3jv6gfjofooseq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220111205503/https://ieeexplore.ieee.org/ielx7/78/9307529/09675299.pdf?tp=&amp;arnumber=9675299&amp;isnumber=9307529&amp;ref=" 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/53/0a/530a9a2273d9ad679f0a0033f08031fcb1efa034.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tsp.2021.3136798"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Community Detection in Partially Observable Social Networks [article]

Cong Tran, Won-Yong Shin, Andreas Spitz
<span title="2021-04-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks' topology and functions.  ...  To solve this problem, we introduce KroMFac, a new framework that conducts community detection via regularized nonnegative matrix factorization (NMF) based on the Kronecker graph model.  ...  To reduce the complexity, we take advantage of the property that real-world social networks usually have a sparse and low-rank matrix structure [39] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1801.00132v8">arXiv:1801.00132v8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lcylh3shrzdgfn3i6bnei5fd5i">fatcat:lcylh3shrzdgfn3i6bnei5fd5i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210420015415/https://arxiv.org/pdf/1801.00132v8.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/ed/bb/edbb2958f0bbcdeb41b9b5444b0e6c7eb6a2b3a5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1801.00132v8" 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>

Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information

Rasoul Shafipour, Gonzalo Mateos
<span title="2020-09-09">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/63zsvf7vxzfznojpqgfvpyk2lu" style="color: black;">Algorithms</a> </i> &nbsp;
Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired.  ...  Numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network.  ...  Examples include social network studies involving questionnaire-based random sampling designs ([1], Ch. 5.3), or experimental testing of suspected regulatory interactions among selected pairs of genes  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/a13090228">doi:10.3390/a13090228</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3jmsr6r6jfayhgmkbcduzmf5ji">fatcat:3jmsr6r6jfayhgmkbcduzmf5ji</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200919072540/https://res.mdpi.com/d_attachment/algorithms/algorithms-13-00228/article_deploy/algorithms-13-00228.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/e8/05/e805a235077d2b652accc88884c6dffbd4c822e8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/a13090228"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Sampling and Inference of Networked Dynamics using Log-Koopman Nonlinear Graph Fourier Transform [article]

Zhuangkun Wei, Bin Li, Chengyao Sun, Weisi Guo
<span title="2020-10-16">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, current polynomial Koopman operators result in a large sampling space due to: (i) the large size of polynomial based observables (O(N^2), N number of nodes in network), and (ii) not factoring  ...  As a result, one can derive a sampling strategy via the linear evolution property of observable.  ...  These approaches all provide attractive performances on sampling node compression and state recovery accuracy, under the important premise of a known and linear/linearized underlying model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.09615v2">arXiv:2004.09615v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6xsnlxqokzggzhwdtqfklthkwe">fatcat:6xsnlxqokzggzhwdtqfklthkwe</a> </span>
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Sparse and Compositionally Robust Inference of Microbial Ecological Networks

Zachary D. Kurtz, Christian L. Müller, Emily R. Miraldi, Dan R. Littman, Martin J. Blaser, Richard A. Bonneau, Christian von Mering
<span title="2015-05-07">2015</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ch57atmlprauhhbqdf7x4ytejm" style="color: black;">PLoS Computational Biology</a> </i> &nbsp;
SPIEC-EASI outperforms state-of-the-art methods in terms of edge recovery and network properties on realistic synthetic data under a variety of scenarios.  ...  Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU interaction networks is severely under-powered, and additional  ...  Acknowledgments We would like to thank Eric Alm and Jonathan Friedman for helpful discussions. Author Contributions  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pcbi.1004226">doi:10.1371/journal.pcbi.1004226</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25950956">pmid:25950956</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4423992/">pmcid:PMC4423992</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vmwmoxocwje3zdukfr76q2rxuu">fatcat:vmwmoxocwje3zdukfr76q2rxuu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171015145457/http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004226&amp;type=printable" 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/d2/8f/d28fa87dc589e4a85a06f18ae8f71d9451f653f9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pcbi.1004226"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423992" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Blind identification of graph filters with sparse inputs

Santiago Segarra, Gonzalo Mateos, Antonio G. Marques, Alejandro Ribeiro
<span title="">2015</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fl5ojmjvcnfondavoy46ujryxm" style="color: black;">2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)</a> </i> &nbsp;
While the filtered graph signal is a bilinear function of x and h, y is also a linear combination of the entries of the rank-one matrix xh T .  ...  This paper deals with the problem of blind graph filter identification, which finds applications in social and brain networks, to name a few.  ...  the rank-one matrix xh T .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/camsap.2015.7383833">doi:10.1109/camsap.2015.7383833</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/camsap/SegarraMMR15.html">dblp:conf/camsap/SegarraMMR15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s5ahg7fg3vhvbmmybqdxvo55ny">fatcat:s5ahg7fg3vhvbmmybqdxvo55ny</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20151203130426/http://www.seas.upenn.edu/~aribeiro/preprints/c_2015_segarra_etal_e.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/4c/b9/4cb9aa3f6ffccd8775f59cefbffe81af3ef2c311.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/camsap.2015.7383833"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Binary matrix completion with nonconvex regularizers [article]

Chunsheng Liu
<span title="2019-04-08">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Many practical problems involve the recovery of a binary matrix from partial information, which makes the binary matrix completion (BMC) technique received increasing attention in machine learning.  ...  Extensive experiments conducted on both synthetic and real-world data sets demonstrate the superiority of the proposed approach over other competing methods.  ...  Related work In the last decade, based on the remarkable result of low-rank matrix completion [1] , a tremendous amount of work has focused on the problem, which enabled a burst of progress concerning  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.03807v1">arXiv:1904.03807v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vax3knssmnfczbw3dcd43anktm">fatcat:vax3knssmnfczbw3dcd43anktm</a> </span>
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Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks

Yanning Shen, Brian Baingana, Georgios B. Giannakis
<span title="2017-07-15">2017</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gkn2pu46ozb4tmkxczacnmtvkq" style="color: black;">IEEE Transactions on Signal Processing</a> </i> &nbsp;
Extensive tests on simulated and real stock quote data demonstrate the merits of the novel tensor-based approach.  ...  The present paper advocates a novel SEM-based topology inference approach that entails factorization of a three-way tensor, constructed from the observed nodal data, using the well-known parallel factor  ...  On the other hand, in the completely blind case, Algorithm 1 still results in a reliable estimate of the network topology with low edge identification error.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tsp.2017.2698369">doi:10.1109/tsp.2017.2698369</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yfttxxth6bbh3jhnfrrhmfp5xe">fatcat:yfttxxth6bbh3jhnfrrhmfp5xe</a> </span>
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Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information [article]

Rasoul Shafipour, Gonzalo Mateos
<span title="2020-07-07">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our goal is to track the (possibly) time-varying network topology, and effect memory and computational savings by processing the data on-the-fly as they are acquired.  ...  Numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network.  ...  , COVID-19 incidence in different geographical regions linked via mobility or social graphs, and fake news that spread on online social networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.03653v1">arXiv:2007.03653v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7nht6r47wfgjxcauzexvlnun4a">fatcat:7nht6r47wfgjxcauzexvlnun4a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200710070205/https://arxiv.org/pdf/2007.03653v1.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] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.03653v1" 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>

Blind identification of stochastic block models from dynamical observations [article]

Michael T. Schaub and Santiago Segarra and John N. Tsitsiklis
<span title="2019-12-03">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We consider a blind identification problem in which we aim to recover a statistical model of a network without knowledge of the network's edges, but based solely on nodal observations of a certain process  ...  More concretely, we focus on observations that consist of single snapshots taken from multiple trajectories of a diffusive process that evolves over the unknown network.  ...  to a network whose expected adjacency matrix has a low-rank block structure (rank k).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.09107v2">arXiv:1905.09107v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/obld4asbmjftxflddc7pktba6i">fatcat:obld4asbmjftxflddc7pktba6i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200909043234/https://arxiv.org/pdf/1905.09107v2.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/96/39/96391800d1f921c6e5cfd23d699b94890ac089fc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.09107v2" 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>

Using LTI Dynamics to Identify the Influential Nodes in a Network

Goran Murić, Eduard Jorswieck, Christian Scheunert, Peter Csermely
<span title="2016-12-28">2016</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
spreader, the one that is capable of propagating information in the shortest time to a large portion of the network.  ...  We utilize the system-theoretic approach considering the network as a Linear Time-Invariant system. By observing the system response we can quantify the importance of each node.  ...  The matrix A determines the dynamics of the system and it can be obtained as a transpose of the adjacency matrix describing the network topology A ¼ A T adj [18, 19] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0168514">doi:10.1371/journal.pone.0168514</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28030548">pmid:28030548</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5193404/">pmcid:PMC5193404</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tsnfz7hqn5fgzhyrv73dvmmbly">fatcat:tsnfz7hqn5fgzhyrv73dvmmbly</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171012055252/http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0168514&amp;type=printable" 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/f3/cb/f3cb7c7d9677c1a92083eb4afff5781f1e92bea9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0168514"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5193404" 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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