{"id":13,"date":"2015-06-15T10:26:53","date_gmt":"2015-06-15T08:26:53","guid":{"rendered":"http:\/\/members.loria.fr\/thierrygartiser\/?page_id=13"},"modified":"2026-02-05T12:42:02","modified_gmt":"2026-02-05T10:42:02","slug":"publications","status":"publish","type":"page","link":"https:\/\/members.loria.fr\/SAridhi\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"<p><strong><b><p><a href=\"https:\/\/www.google.fr\/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=2&amp;cad=rja&amp;uact=8&amp;ved=0ahUKEwiL7JzwnP7OAhVIuhoKHfm9B_gQFggjMAE&amp;url=http%3A%2F%2Fscholar.google.fr%2Fcitations%3Fuser%3DJ_g11soAAAAJ%26hl%3Den&amp;usg=AFQjCNHRVrPUKwHCBLd3XSeeUGHS_IwEPw&amp;sig2=MM16kOReEnZ54msxjM-3bg\" target=\"_blank\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-425 alignleft\" src=\"\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2016\/09\/logo_google_scholar.gif\" alt=\"logo_google_scholar\" width=\"257\" height=\"53\" \/><\/a><a href=\"http:\/\/dblp.uni-trier.de\/pers\/hd\/a\/Aridhi:Sabeur\" target=\"_blank\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-424 alignright\" src=\"\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2016\/09\/logo_dblp-300x117.png\" alt=\"logo_dblp\" width=\"167\" height=\"65\" srcset=\"https:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2016\/09\/logo_dblp-300x117.png 300w, https:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2016\/09\/logo_dblp.png 384w\" sizes=\"auto, (max-width: 167px) 100vw, 167px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<\/b><\/strong><\/p>\n<hr \/>\n<p><b>Books and Book Chapters<br \/>\n<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[B3] <\/b><\/strong><\/span>M.K. Islam, <strong>S. Aridhi<\/strong>, M. Smail-Tabbone. (2022). From Competition to Collaboration: Ensembling Similarity-Based Heuristics for Supervised Link Prediction in Biological Graphs. In: <em>Communications in Computer and Information Science<\/em>, vol 1550. Springer, Cham.<\/p>\n<p><strong><b><span style=\"color: #0000ff\">[B2]<\/span> <\/b><\/strong>M. Zoghlami, <strong>S. Aridhi<\/strong>, M. Maddouri and E. Mephu Nguifo. An Overview of in Silico Methods for the Prediction of Ionizing Radiation Resistance in Bacteria. In:\u00a0<em>Ionizing Radiation: Advances in Research and Applications<\/em>, <em>Physics Research and Technology Series<\/em>, \u00a0ISBN: 978-1-53613-539-8, 2018.<\/p>\n<p><span style=\"color: #0000ff\"><strong><b><a href=\"http:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2018\/03\/book.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-812 alignright\" src=\"http:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2018\/03\/book-202x300.jpg\" alt=\"\" width=\"124\" height=\"184\" srcset=\"https:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2018\/03\/book-202x300.jpg 202w, https:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2018\/03\/book.jpg 336w\" sizes=\"auto, (max-width: 124px) 100vw, 124px\" \/><\/a>[B1]<\/b><\/strong><\/span> <strong>S. Aridhi<\/strong>, P. Lacomme, R. Phan. Bases de donn\u00e9es NoSQL et Big Data : Concevoir des bases de donn\u00e9es pour le Big Data. Editeur : <a href=\"http:\/\/www.editions-ellipses.fr\/\" target=\"_blank\" rel=\"noopener noreferrer\">Ellipses<\/a>, ISBN : 2340002613, D\u00e9cembre 2014.<\/p>\n<ul>\n<li><a href=\"http:\/\/www.isima.fr\/~lacomme\/NoSQL\/\" target=\"_blank\" rel=\"noopener noreferrer\">Web page of the book<\/a><\/li>\n<li><a href=\"https:\/\/www.amazon.fr\/Bases-donn%C3%A9es-NoSQL-Big-Data\/dp\/2340002613\/ref=asap_bc?ie=UTF8\" target=\"_blank\" rel=\"noopener noreferrer\">Get the book!<\/a><\/li>\n<li><a href=\"https:\/\/www.youtube.com\/channel\/UCDiv_uHPsbPzEWUiRvEiODg\" target=\"_blank\" rel=\"noopener noreferrer\">Youtube channel<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Proceedings and editorials<\/b><br \/>\n<b><\/b><\/p>\n<p><strong><b><span style=\"color: #0000ff\">[P3]<\/span> <\/b><\/strong>A. Hadjali, H. Mezni, <strong>S. Aridhi<\/strong>, A. Tchernykh.\u00a0 Special issue on \u201cUncertainty in Cloud Computing: Concepts, Challenges and Current Solutions\u201d.\u00a0 <em>International Journal of Approximate Reasoning<\/em>, <em>Elsevier, 129, pp. 53-55, 2019. <\/em><strong>[IF=3.768]<\/strong><strong><b><br \/>\n<\/b><\/strong><\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[P2] <\/b><\/span>S. Aridhi<\/strong>, J.A. Fernandes de Mac\u00eado, E. Mephu Nguifo and K. Zeitouni. <span class=\"title\">Proceedings of the Workshop on Large-Scale Time Dependent Graphs (TD-LSG 2018) co-located with the 44th International Conference on Very Large Data Bases (VLDB 2018), Rio de Janeiro, Brazil, Aug. 27, 2018.<\/span><\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[P1] <\/b><\/span>S. Aridhi<\/strong>, J.A. Fernandes de Mac\u00eado, E. Mephu Nguifo and K. Zeitouni. <span class=\"title\">Proceedings of the Workshop on Large-Scale Time Dependent Graphs (TD-LSG 2017) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017), Skopje, Macedonia, Sep. 18, 2017.<\/span><\/p>\n<p><b>Papers in journals with reviewing committee<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong>[J24]<\/strong><\/span> N.E.I. Karabadji, A. Korba, A. Assi, H. Seridi, <strong>S. Aridhi<\/strong>, W. Dhifli. Accuracy and Diversity-Aware Multi-Objective Approach for Random Forest Construction. Expert Systems With Applications Elsevier, 2023, 225 (1), pp.120138. <strong>[IF=8.66]<\/strong><\/p>\n<p><strong><b><span style=\"color: #0000ff\">[J23]<\/span> <\/b><\/strong>K. Islam, D. Ramirez, B. Maigret, M.D. Devignes, <strong>S. Aridhi<\/strong>, M. Smail-Tabbone. Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding. <i>Sci Rep<\/i> <b>13<\/b>, Nature, 3643 (2023). https:\/\/doi.org\/10.1038\/s41598-023-30095-z<\/p>\n<p><strong><b><span style=\"color: #0000ff\">[J22]<\/span> <\/b><\/strong>B. Sarker, D.W. Ritchie and <strong>S. Aridhi<\/strong>. Improving Automatic GO Annotation With Semantic Similarity.<em> BMC Bioinformatics<\/em>, 2022, 23 (S2), pp.433.\u00a0 <strong>[IF=2.511]<\/strong><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[J21]<\/b><\/strong> <\/span>W. Inoubli,\u00a0<strong>S. Aridhi<\/strong>, H. Mezni, M. Maddouri, E. M. Nguifo. A distributed and incremental algorithm for large-scale graph clustering. <em>Future Generation Computer Systems, Elsevier<\/em>, <em>134, pp. 334-347, 2022<\/em>. <b>[IF = 7.30]<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[J20] <\/b><\/strong><\/span>K. Islam, <strong>S. Aridhi<\/strong>, M. Smail-Tabbone. Negative sampling and rule mining for explainable link prediction in knowledge graphs. <em>Knowledge-Based Systems (KBS), <\/em>109083, 2022. <strong>[IF=8.03]<\/strong><\/p>\n<div class=\"gsc_oci_value\"><span style=\"color: #0000ff\"><strong><b>[J19]<\/b><\/strong> <\/span>Marcelo BA Veras, Bishnu Sarker, Sabeur Aridhi, Jo\u00e3o PP Gomes, Jos\u00e9 AF Mac\u00eado, Engelbert Mephu Nguifo, Marie-Dominique Devignes, Malika Sma\u00efl-Tabbone. On the design of a similarity function for sparse binary data with application on protein function annotation. <em>Knowledge-Based Systems, 2022, 238, pp.107863.<\/em> <strong>[IF=8.03]<\/strong><\/div>\n<div><\/div>\n<div><span style=\"color: #0000ff\"><span style=\"color: #0000ff\"><strong><b>[J18] <\/b><\/strong><\/span><\/span>K. Islam, S. Aridhi, M. Smail-Tabbone. An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs. <em>International Journal of Data Mining &amp; Knowledge Management Process<\/em> 11(05):1-18.<\/div>\n<p><strong><b><span style=\"color: #0000ff\">[J17]<\/span> <\/b><\/strong>H. Mezni, M. Sellami, S. Aridhi, F. Ben-Charrada<i>.<\/i> Towards big services: a synergy between service computing and parallel programming. <i>Computing<\/i> <b>103, <\/b>2479\u20132519, 2021.\u00a0 <strong>[IF=2.22]<\/strong><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[J16] <\/b><\/strong><\/span>B. Sarker, D.W. Ritchie and <strong>S. Aridhi<\/strong>. GrAPFI: Predicting Enzymatic Function of Proteins From Domain Similarity Graphs.<em> BMC Bioinformatics <\/em><b>21, <\/b>168, 2020. [<a href=\"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-3460-7\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] <strong>[IF=2.511]<\/strong><\/p>\n<p><span style=\"color: #0000ff\"><strong>[J15]<\/strong> <\/span>M. Zoghlami, <strong>S. Aridhi<\/strong>, M. Maddouri and E. Mephu Nguifo<em>. <\/em>Multiple instance learning for sequence data with across bag dependencies<em>.<\/em>\u00a0<em>International Journal of Machine Learning and Cybernetics,\u00a0 <\/em>11, 629\u2013642, 2020.\u00a0 [<a href=\"http:\/\/homepages.loria.fr\/SAridhi\/software\/MIL\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>] <strong>[IF=3.844]<\/strong><\/p>\n<p><span style=\"color: #0000ff\"><strong>[J14]<\/strong><\/span> N. Zhou, Y. Jiang, &#8230;, <b>S. Aridhi<\/b>, &#8230;, P. Radivojac, I. Friedberg<em>. <\/em><em>The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.<\/em>\u00a0<em>Genome Biology, <\/em>20 (1), 1-23, 2019. <strong>[IF=14.02]<\/strong><\/p>\n<p><span style=\"color: #0000ff\"><strong>[J13]<\/strong><\/span> C. Le Berre, W. J. Sandborn, S. Aridhi, M.D. Devignes, L. Fournier, M. Smail-Tabbone, S. Danese, L. Peyrin-Biroulet<em>. <\/em>Application of Artificial Intelligence to Gastroenterology and Hepatology<em>.<\/em> <em>Gastroenterology, <\/em>158 (1):76-94, 2020.\u00a0 <strong>[IF=20.773]<\/strong><\/p>\n<p><span style=\"color: #0000ff\"><strong>[J12]<\/strong><\/span> N.E.I. Karabadji, I. Khelf<em>,<\/em> H. Seridi, <strong>S. Aridhi<\/strong>, D. Remond, W. Dhifli<em>. <\/em>A Data Sampling and Attribute Selection Strategy for Improving Decision Tree Construction<em>.<\/em> <em>Expert Systems With Applications Elsevier<\/em> , 129, pp. 84-96,-2019. <strong>[IF=8.66]<\/strong><\/p>\n<p><span style=\"color: #0000ff\"><strong>[J11]<\/strong> <\/span>H. Mezni, <strong>S. Aridhi<\/strong>, A. Hadjali.\u00a0The Uncertain Cloud: State of the Art and Research Challenges.\u00a0<em>International Journal of Approximate Reasoning<\/em>, <em>Elsevier, 103, pp. 139-151, 2018<\/em>. [<a href=\"https:\/\/doi.org\/10.1016\/j.ijar.2018.09.009\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] <b>[IF = 1.76]<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[J10]<\/b><\/strong> <\/span>W. Inoubli,\u00a0<strong>S. Aridhi<\/strong>, H. Mezni, M. Maddouri, E. M. Nguifo. An Experimental Survey on Big Data Frameworks. <em>Future Generation Computer Systems, Elsevier<\/em>, <em>86, pp. 546-564, 2018<\/em>.\u00a0 [<a href=\"https:\/\/doi.org\/10.1016\/j.future.2018.04.032\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] [<a href=\"https:\/\/members.loria.fr\/SAridhi\/files\/software\/bigdata\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>] <b>[IF = 7.30]<br \/>\n<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[J9] <\/b><\/strong><\/span>N. Karabadji, S. Beldjoudi, H. Seridi.,\u00a0<strong>S. Aridhi, <\/strong>W. Dhifli. Improving Memory Based User Collaborative Filtering with Evolutionary Multi-Objective Optimization. <em>Expert Systems With Applications (ESWA), Elsevier, 98, pp.153-165, 2018<\/em><em>.\u00a0<\/em>[<a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2018.01.015\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] <b>[IF = 8.66]<br \/>\n<\/b><\/p>\n<p><strong><b><span style=\"color: #0000ff\">[J8]<\/span> <\/b>S. Aridhi<\/strong>, A. Montresor, Y. Velegrakis. BLADYG: A Graph Processing Framework for Large Dynamic Graphs. <i>Big Data Research (BDR), Elsevier<\/i>, 9(C), pp. 9-17, 2017. [<a href=\"https:\/\/doi.org\/10.1016\/j.bdr.2017.05.003\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] [<a href=\"http:\/\/bladyg.loria.fr\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>] <strong>[IF=3.57]<\/strong><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[J7]<\/b><\/strong><\/span> W. Dhifli,\u00a0<strong>S. Aridhi<\/strong>, E. M. Nguifo.\u00a0MR-SimLab: Scalable Subgraph Selection with Label Similarity for Big Data. <em>Information Systems, Elsevier<\/em>, 2017, 69, pp. 155-163, 2017. [<a href=\"http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306437916304033\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] <b>[IF = 2.3]<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[J6] <\/b><\/strong><\/span>N. Karabadji, H. Seridi., F. Bousetouane, W. Dhifli.,\u00a0<strong>S. Aridhi<\/strong>. An Evolutionary Scheme for Decision Tree Construction. <em>Knowledge-Based Systems (KBS), Elsevier, 116, pp. 166-177, <\/em>2017<em>.<\/em> [<a href=\"http:\/\/dx.doi.org\/10.1016\/j.knosys.2016.12.011\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>]<b> [<span style=\"color: #ff0000\"><a style=\"color: #ff0000\" href=\"http:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2019\/01\/Honourable_Mention_CS_-Karabadji.pdf\">Honourable Mention in the 2017 Algerian Paper of the Year Awards<\/a><\/span>] [IF = <strong>8.03<\/strong>]<br \/>\n<\/b><\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[J5]<\/b><\/span> S. Aridhi<\/strong> and E. Mephu Nguifo. Big Graph Mining: Frameworks and Techniques. <i>Big Data Research (BDR), Elsevier<\/i>, 6, pp. 1-10, 2016. [<a href=\"http:\/\/dx.doi.org\/10.1016\/j.bdr.2016.07.002\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] <strong>[IF=3.57]<\/strong><\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[J4]<\/b> <\/span>S. Aridhi<\/strong>, H. Sghaier, M. Zoghlami, M. Maddouri and E. Mephu Nguifo. Prediction of Ionizing Radiation Resistance in Bacteria using a multiple instance learning model. <i>Journal of Computational Biology (JCB)<\/i>, 23(1): pp. 10-20, 2016. [<a href=\"http:\/\/dx.doi.org\/10.1089\/cmb.2015.0134\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] [<a href=\"http:\/\/homepages.loria.fr\/SAridhi\/software\/MIL\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>] <b>[IF = 1.03]<\/b><\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[J3]<\/b> <\/span>S. Aridhi<\/strong>, P. Lacomme, L. Ren, B. Vincent. A MapReduce-based approach for shortest path problem in large-scale networks. <i>Engineering Applications of Artificial Intelligence, Elsevier<\/i>, 41, pp. 151-165, 2015, ISSN 0952-1976. [<a href=\"http:\/\/dx.doi.org\/10.1016\/j.engappai.2015.02.008\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] [<a href=\"http:\/\/www.isima.fr\/~lacomme\/OR_hadoop\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>] <b> [IF = 2.89]<\/b><\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[J2]<\/b> <\/span>S. Aridhi<\/strong>, L. d&rsquo;Orazio, M. Maddouri and E. Mephu Nguifo. Density-based data partitioning strategy to approximate large-scale subgraph mining. <i>Information Systems, Elsevier<\/i>, 48, pp. 213-223, 2015, ISSN 0306-4379. [<a href=\"http:\/\/dx.doi.org\/10.1016\/j.is.2013.08.005\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] [<a href=\"http:\/\/www.isima.fr\/~aridhi\/DistFSM\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>] <b> [IF = 2.3]<\/b><\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[J1]<\/b> <\/span>S. Aridhi<\/strong>, L. d&rsquo;Orazio, M. Maddouri and E. Mephu Nguifo. Un partitionnement bas\u00e9 sur la densit\u00e9 de graphe pour approcher la fouille distribu\u00e9e de sous-graphes fr\u00e9quents. <i>Technique et Science Informatiques<\/i>, 33(9-10), pp. 711-737, 2014. [<a href=\"http:\/\/tsi.revuesonline.com\/article.jsp?articleId=20268\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>]<\/p>\n<p><b>International conferences\/workshops with program committee<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC27]<\/b><\/strong> <\/span>A. Bajracharya, Y. Toussaint, <strong>S. Aridhi<\/strong>. <em>Comparative Analysis of Multimodal Fusion Techniques for Clinical Severity Classification. in Proceedings of the 5th International Workshop on Multi-Modal Medical Data Analysis, co-located with IEEE International Conference on Big Data (IEEE Big Data 2025), IEEE, 2025, pp. 6218\u20136227.<\/em><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC26]<\/b><\/strong><\/span> W. Sellami, W. Inoubli, I. Farah, <strong>S. Aridhi<\/strong>. <em>SA-KGP: A Semantic-Aware Partitioning Method for Scalable Knowledge Graph Embedding<\/em>. in <em data-start=\"816\" data-end=\"899\">Proceedings of the 5th Workshop on Knowledge Graphs and Big Data (KGBigData 2025)<\/em>, co-located with IEEE International Conference on Big Data (IEEE Big Data 2025), IEEE, 2025, pp. 5697\u20135706.<\/p>\n<div id=\"gsc_oci_title_wrapper\">\n<div><span style=\"color: #0000ff\"><strong><b>[IC25]<\/b><\/strong> <\/span>A Sellami, B Sarker, S Tabbone, MD Devignes, <strong>S Aridhi. <\/strong>A Semi-supervised Graph Deep Neural Network for Automatic Protein Function Annotation. <em>International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO), Gran Canaria, 2022.<\/em><\/div>\n<div><\/div>\n<div><span style=\"color: #0000ff\"><strong><b>[IC24] <\/b><\/strong><\/span>K. Islam, S. Aridhi, M. Smail-Tabbone. Simple negative sampling for link prediction in knowledge graphs. <em>Proceedings of the 10th International Conference on Complex Networks and Their Applications, Madrid, <\/em><em>2021<\/em>.<strong><b><br \/>\n<\/b><\/strong><\/div>\n<div><\/div>\n<div id=\"gsc_oci_title\"><span style=\"color: #0000ff\"><strong><b>[IC23] <\/b><\/strong><\/span>K. Islam, S. Aridhi, M. Smail-Tabbone. <em>Appraisal Study of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs. <i>10th International Conference on Data Mining &amp; Knowledge Management Process (CDKP 2021)<\/i><\/em>, Jul 2021, London, United Kingdom. pp.81-92.<\/div>\n<\/div>\n<p><span style=\"color: #0000ff\"><strong><b>[IC22] <\/b><\/strong><\/span>K. Islam, S. Aridhi, M. Smail-Tabbone. A comparative study of similarity-based and GNN-based link prediction approaches. In <i>Proceedings of the\u00a0 International Workshop <\/i><em>on Graph Embedding and Mining<\/em> (GEM) <em>in conjunction with ECML-PKDD 2020, Ghent, Belgium. <\/em><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC21] <\/b><\/strong><\/span>B. Sarker, N. Khare, M.D. Devignes and <strong>S. Aridhi<\/strong>. Graph Based Automatic Protein Function Annotation Improved By Semantic Similarity.<em> Proceedings of the 8th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2020), Granada, <\/em><em>2020<\/em>.<\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC20] <\/b><\/strong><\/span>B. Sarker, D.W. Ritchie and <strong>S. Aridhi<\/strong>. Functional Annotation of Proteins using Domain Embedding based Sequence Classification.<em> Proceedings of the 11th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2019), Vienna, <\/em><em>2019<\/em>.<\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC19] <\/b><\/strong><\/span>M. Zoghlami, <strong>S. Aridhi<\/strong>, M. Maddouri and E. Mephu Nguifo. A Structure Based Multiple Instance Learning Approach for Bacterial Ionizing Radiation Resistance Prediction. In <i>Proceedings of the <\/i>23rd International Conference on Knowledge-Based and Intelligent Information &amp; Engineering Systems (KES 2019), Budapest, 2019. [<a href=\"http:\/\/homepages.loria.fr\/SAridhi\/software\/MIL\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>]<\/p>\n<p><strong><b><span style=\"color: #0000ff\">[IC18]<\/span>\u00a0<\/b><\/strong>B. Sarker, D.W. Ritchie and <strong>S. Aridhi<\/strong>. Exploiting Complex Protein Domain Networks for Protein Function Annotation.<em> Proceedings of the 7th International Conference on Complex Networks and Their Applications, Cambridge, <\/em><em>2018<\/em>.<\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC17]\u00a0<\/b><\/strong><\/span>B. Sarker, D.W. Ritchie and <strong>S. Aridhi<\/strong>. GrAPFI: Graph Based Inference for Automatic Protein Function Annotation.<em> 17th European Conference on Computational Biology (ECCB)<\/em> <em>2018, Athens, Greece<\/em>. <b>(<a href=\"http:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2018\/09\/Sarker-et-al-ECCB-2018-min.pdf\">poster<\/a>)<\/b><\/p>\n<p><strong><b><span style=\"color: #0000ff\">[IC16]<\/span> <\/b><\/strong>S.Z. Alborzi, <strong>S. Aridhi<\/strong>,\u00a0D.W. Ritchie and M.D. Devignes. PPI DomainMiner: predicting domain-domain interactions from protein-protein interactions using tripartite graph modeling and vector similarity.\u00a0<em>17th European Conference on Computational Biology (ECCB)<\/em> <em>2018, Athens, Greece<\/em>. <b>(poster)<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC15]<\/b> <\/strong><\/span>W. Inoubli,<strong> S. Aridhi<\/strong>, H. Mezni, M. Maddouri and E. Mephu Nguifo. A Comparative Study on Streaming Frameworks for Big Data .<em> Proceedings of the Latin America Data Science Workshop co-located with 44th International Conference on Very Large Data Bases (VLDB 2018), Rio de Janeiro, Brazil, Aug 27, 2018<\/em>. [<a href=\"https:\/\/members.loria.fr\/SAridhi\/files\/software\/bigdata\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>]<\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC14]<\/b> <\/strong><\/span>W. Inoubli,<strong> S. Aridhi<\/strong>, H. Mezni, M. Maddouri and E. Mephu Nguifo. An Experimental Survey on Big Data Frameworks\u00a0. <em>Extremely Large Databases Conference (XLDB)<\/em> 2017, Clermont Ferrand, France. <b>(Lightning talk, <a href=\"http:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2017\/10\/Inoubli-et-al-XLDB-poster.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">poster<\/a>)<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC13]<\/b><\/strong><\/span> W. Inoubli, L. Almada, T.L. Coelho da Silva, G. Coutinho, L. Peres, R.P. Magalhaes, J.F. de Macedo,\u00a0 <strong>S. Aridhi<\/strong>, E. Mephu Nguifo. A Distributed Framework for Large-Scale Time-Dependent Graph Analysis. <em>Joint Workshop on Large-Scale Evolving Networks and Graphs in conjunction with ECML-PKDD 2017, Skopje, Macedonia<\/em>.<span style=\"color: #000000\"><br \/>\n<\/span><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC12]\u00a0<\/b> <\/strong><\/span><strong>S. Aridhi<\/strong>, S.Z. Alborzi, M.S. Tabbone, M.D. Devignes and D.W. Ritchie. Neighborhood-Based Label Propagation in Large Protein Graphs. <em>Function SIG@ISMB-ECCB<\/em> <em>2017, Prague, Czech Republic<\/em>.<\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[IC11] <\/b><\/strong><\/span>S.Z. Alborzi, <strong>S. Aridhi<\/strong>, M.D. Devignes, R. Saidi, A. Renaux, M.J. Martin and D.W. Ritchie. Automatic Generation of Functional Annotation Rules Using Inferred GO-Domain Associations. <em>Function SIG@ISMB-ECCB<\/em> <em>2017, Prague, Czech Republic<\/em>.<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[IC10]<\/b> <\/span>S. Aridhi<\/strong>, H. Sghaier, M. Zoghlami, M. Maddouri and E. Mephu Nguifo. Prediction of ionizing radiation resistance in bacteria using a multiple instance learning model. In <i>Proceedings of the 2nd International Workshop on Advances in Bioinformatics and Artificial Intelligence: Bridging the Gap (BAI &rsquo;16) @ IJCAI\u201916,<\/i> New York, USA. (<strong>Highlight paper<\/strong>) [<a href=\"http:\/\/homepages.loria.fr\/SAridhi\/software\/MIL\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[IC9]<\/b> <\/span>S. Aridhi<\/strong>, M. Brugnara,A. Montresor, Y. Velegrakis. Distributed k-core Decomposition and Maintenance in Large Dynamic Graphs. In <i>Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems (DEBS\u201916)<\/i>, pp. 161-168, Irvine, 2016. [<a href=\"http:\/\/doi.acm.org\/10.1145\/2933267.2933299\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] [<a href=\"http:\/\/db.disi.unitn.eu\/pages\/dkcore\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[IC8]<\/b><\/span> S. Aridhi<\/strong>, A. Montresor, Y. Velegrakis. BLADYG: A novel block-centric framework for the analysis of large dynamic graphs. In <i> Proceedings of the ACM Workshop on High Performance Graph Processing (HPGP\u201916) @ HPDC\u201916<\/i>, pp. 39-42, Kyoto, Japan, 2016. [<a href=\"http:\/\/dx.doi.org\/10.1145\/2915516.2915525\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] [<a href=\"http:\/\/bladyg.loria.fr\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[IC7]<\/b><\/span><\/strong> C. Sakouhi, <strong>S. Aridhi<\/strong>, A. Guerrieri, S. Sassi and A. Montresor. <span class=\"st\">DynamicDFEP: A distributed edge partitioning approach for large dynamic graphs.<\/span> In <i>Proceedings of the 20th International Database Engineering &amp; Applications Symposium (IDEAS\u201916)<\/i>, pp. 61-168, Montreal, Canada, 2016. [<a href=\"http:\/\/dx.doi.org\/10.1145\/2938503.2938506\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[IC6]<\/b><\/span><\/strong> N. Karabadji, <strong>S. Aridhi<\/strong>, H. Seridi. A Frequent Closed Connected Subgraph Mining Algorithm in Unique Edge Label Graphs. In <i>Proceedings of the 12th International Conference on Machine Learning and Data Mining (MLDM\u201916)<\/i>, pp. 43-57, New York, USA, 2016. [<a href=\"http:\/\/dx.doi.org\/10.1007\/978-3-319-41920-6_4\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[IC5] <\/b><\/span><\/strong><strong>S. Aridhi<\/strong>, L. d&rsquo;Orazio, M. Maddouri and E. Mephu Nguifo. Cost Models for Distributed Pattern Mining in the Cloud. In <i>Proceedings of IEEE International Conference on Big Data Science and Engineering<\/i>, IEEE, pp. 112-119, Helsinki, Finland, 2015. [<a href=\"http:\/\/dx.doi.org\/10.1109\/Trustcom.2015.569\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[IC4]<\/b><\/span> S. Aridhi<\/strong>, B. Vincent, P. Lacomme and L. Ren. Shortest Path Resolution Using Hadoop. <i>10th International Conference on Modeling, Optimization and Simulation (MOSIM &rsquo;14)<\/i>, Nancy, France, 2014.<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[IC3]<\/b><\/span> S. Aridhi<\/strong>, H. Sghaier, M. Maddouri and E. Mephu Nguifo. Computational phenotype prediction of ionizing-radiation-resistant bacteria with a multiple-instance learning model. In <i>Proceedings of the 12th International Workshop on Data Mining in Bioinformatics (BioKDD &rsquo;13),<\/i> pp. 18-24<i>, <\/i>Chicago<i>,<\/i> USA. [<a href=\"http:\/\/doi.acm.org\/10.1145\/2500863.2500866\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>] [<a href=\"http:\/\/www.isima.fr\/~mephu\/IRR\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[IC2]<\/b><\/span><\/strong> R. Saidi, <strong>S. Aridhi<\/strong>, M. Maddouri, E. Mephu Nguifo. Feature extraction in protein sequence classification : a new stability measure. In <i>Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine (BCB &rsquo;12)<\/i>. ACM, New York, NY, USA, 683-689. [<a href=\"http:\/\/dl.acm.org\/citation.cfm?id=2383060&amp;dl=ACM&amp;coll=DL&amp;CFID=220319568&amp;CFTOKEN=99409971\" target=\"blanc\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[IC1]<\/b><\/span><\/strong> R. Saidi, <strong>S. Aridhi<\/strong>, M. Maddouri et E. Mephu Nguifo. Etude de stabilit\u00e9 de m\u00e9thodes de s\u00e9lection de motifs \u00e0 partir des s\u00e9quences prot\u00e9iques. In <i>Proceedings of \u00ab\u00a0Conf\u00e9rence internationale sur l&rsquo;extraction et la gestion des connaissances\u00a0\u00bb (EGC &rsquo;10)<\/i>, 703-704, 2010, Hammamet, Tunisia. [<a href=\"http:\/\/editions-rnti.fr\/?inprocid=1001454&amp;lg=en\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>]<\/p>\n<p><b>International symposiums with program committee<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[S4]<\/b><\/strong><\/span> C. Arouri, E. Mephu Nguifo, <strong>S. Aridhi<\/strong>, C. Roucelle, G. Bonnet-Loosli, N. Tsopz\u00e9. Towards a constructive multilayer perceptron for regression task using non-parametric clustering. A case study of Photo-Z redshift reconstruction.\u00a0<em>European Week of Astronomy and Space Science (<a href=\"http:\/\/eas.unige.ch\/EWASS2017\/\">EWASS 2017<\/a>)<\/em>, Prague, Czech republic, 2017. [<a href=\"http:\/\/arxiv.org\/abs\/1412.5513\" target=\"_blank\" rel=\"noopener noreferrer\">arXiv:1412.5513<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[S3]<\/b><\/span> S. Aridhi<\/strong>, H. Sghaier, M. Maddouri et E. Mephu Nguifo. Domain knowledge-based model for phenotype prediction of ionizing-radiation-resistance in bacteria. <i>ISCB Student Council Symposium 2014 meeting<\/i>, Strasbourg, France. [<a href=\"http:\/\/f1000.com\/posters\/browse\/summary\/1096838\" target=\"blanc\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[S2]<\/b><\/span> S. Aridhi<\/strong>, L. d&rsquo;Orazio, M. Maddouri and E. Mephu Nguifo. A novel MapReduce-based approach for distributed frequent subgraph mining. <i>Machine Learning and Data Analytics Symposium (MLDAS) 2014<\/i>, Doha, Qatar.<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[S1]<\/b><\/span> S. Aridhi<\/strong>, H. Sghaier, M. Maddouri et E. Mephu Nguifo. <em>in silico<\/em> phenotype prediction of ionizing-radiation-resistant bacteria by extraction of discriminative motifs. <i>ISCB Student Council Symposium 2011 meeting<\/i>, Vienna, Austria. [<a href=\"http:\/\/f1000.com\/posters\/browse\/summary\/2036\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>]<\/p>\n<p><b>National conferences with program committee<\/b><\/p>\n<p><strong><b><span style=\"color: #0000ff\">[NC11]<\/span> <\/b><\/strong>N. Karabadji, H. Seridi, A.A. Korba, <strong>S. Aridhi<\/strong> and W. Dhifli. Optimisation Collective d\u2019Arbres de D\u00e9cision dans une For\u00eat Al\u00e9toire. 36-\u00e8mes journ\u00e9es de la conf\u00e9rence \u00ab\u00a0Gestion de Donn\u00e9es \u2013 Principes, Technologies et Applications\u00a0\u00bb (BDA 2020), Virtual.<\/p>\n<p><strong><b><span style=\"color: #0000ff\">INC10]<\/span> <\/b><\/strong>W. Inoubli, <strong>S. Aridhi<\/strong>, H. Mezni, M. Maddouri, E. M. Nguifo. Un algorithme distribu\u00e9 pour le clustering de grands graphes. <em>Extraction et Gestion des Connaissances (EGC 2020)<\/em>, Bruxelles<em>, Belgique<\/em>.<\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[NC9] <\/b><\/strong><\/span>W. Inoubli,\u00a0<strong>S. Aridhi<\/strong>, H. Mezni, M. Maddouri, E. M. Nguifo. An Experimental Survey on Big Data Frameworks. 34-\u00e8mes journ\u00e9es de la conf\u00e9rence \u00ab\u00a0Gestion de Donn\u00e9es \u2013 Principes, Technologies et Applications\u00a0\u00bb (BDA 2018), Bucarest, Romania. [<a href=\"https:\/\/members.loria.fr\/SAridhi\/files\/software\/bigdata\/\" target=\"_blank\" rel=\"noopener noreferrer\">Supplementary material<\/a>]<\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[NC8] <\/b><\/strong><\/span>M. Zoghlami,<strong> S. Aridhi<\/strong>, M. Maddouri, E. Mephu Nguifo. A multiple instance learning approach for sequence data with across bag dependencies.\u00a0<em>Rencontres des Jeunes Chercheurs en Intelligence Artificielle (RJCIA)<\/em>, Nancy, France, 2018.<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[NC7]<\/b><\/span> S. Aridhi<\/strong>, B. Vincent, P. Lacomme and L. Ren. Taking advantages of the MapReduce paradigm in one hadoop cluster for conception of efficient optimisation method. <i>Workshop on Big Spatial Data<\/i>, Orl\u00e9ans, France, 2014.<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[NC6]<\/b><\/span> S. Aridhi<\/strong>, L. d&rsquo;Orazio, M. Maddouri et E. Mephu Nguifo. A Novel MapReduce-based approach for distributed frequent subgraph mining. <i>19\u00e8me congr\u00e8s national sur la Reconnaissance de Formes et l&rsquo;Intelligence Artificielle (RFIA&rsquo;14)<\/i>, Rouen, France, 2014.<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[NC5]<\/b><\/span> S. Aridhi<\/strong>, L. d&rsquo;Orazio, M. Maddouri et E. Mephu Nguifo. Un partitionnement bas\u00e9 sur la densit\u00e9 de graphe pour approcher la fouille distribu\u00e9e de sous-graphes fr\u00e9quents. <i>Big Data Mining and Visualization<\/i>, Paris, France, 2013. [<a href=\"http:\/\/eric.univ-lyon2.fr\/~gt-fdc\/journees\/edition-2013\/\" target=\"_blank\" rel=\"noopener noreferrer\">Access online<\/a>]<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[NC4]<\/b><\/span> S. Aridhi<\/strong>, L. d&rsquo;Orazio, M. Maddouri et E. Mephu Nguifo. Fouille de sous-graphes fr\u00e9quents dans les nuages. <i>Journ\u00e9e sur le D\u00e9cisionnel dans le Nuage (Cloud BI)<\/i>, Lyon, France, 2013.<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[NC3]<\/b><\/span><\/strong> R. Saidi, W. Dhifli, <strong>S. Aridhi<\/strong>, M. Agier, G. Bronnier, D. Debroas, L. d&rsquo;Orazio, F. Enault, S. Guillaume, E. Mephu Nguifo. Protein classification in the case of large and many-class datasets : A comparison with BLAST and BLAT. <i>Journ\u00e9es Ouvertes en Biologie, Informatique et Math\u00e9matiques (JOBIM)<\/i>, Paris, France, 2011.<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[NC2]<\/b><\/span> S. Aridhi<\/strong>, R. Saidi, M. Maddouri et E. Mephu Nguifo. Etude param\u00e9trique de la stabilit\u00e9 de m\u00e9thodes de s\u00e9lection de motifs \u00e0 partir des s\u00e9quences prot\u00e9iques. <i>17\u00e8me Rencontres de la Soci\u00e9t\u00e9 Francophone de Classification (SFC)<\/i>, Saint-Denis de la R\u00e9union, France, 2010.<\/p>\n<p><strong><span style=\"color: #0000ff\"><b>[NC1]<\/b><\/span><\/strong> R. Saidi, <strong>S. Aridhi<\/strong>, M. Agier, G. Bronnier, D. Debroas, L. d&rsquo;Orazio, F. Enault, S. Guillaume, E. Mephu Nguifo. Functional prediction in the scope of large-scale multi-class learning. <i>Journ\u00e9es Ouvertes en Biologie, Informatique et Math\u00e9matiques (JOBIM)<\/i>, Montpellier, France, 2010. [<a href=\"http:\/\/f1000research.com\/posters\/1681\" target=\"_blank\" rel=\"noopener noreferrer\">Publisher&rsquo;s version<\/a>]<\/p>\n<p><b>Thesis<\/b><\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[T2]<\/b><\/strong> <span style=\"color: #000000\">S.<\/span> <\/span>Aridhi. Distributed frequent subgraph mining in the cloud. Ph.D. thesis , Blaise Pascal University, France, November 2013. [<a href=\"https:\/\/tel.archives-ouvertes.fr\/tel-00951350\" target=\"_blank\" rel=\"noopener noreferrer\">PDF<\/a>]<\/p>\n<p><span style=\"color: #0000ff\"><strong><b>[T1]<\/b><\/strong> <span style=\"color: #000000\">S.<\/span> <\/span>Aridhi. Feature extraction methods in grid computing environments. Master&rsquo;s thesis , University of Jendouba (Tunisia) &#8211; Blaise Pascal University (France), March 2010.<\/p>\n<p><strong><b>\u00a0<\/b><\/strong><\/p>\n<div class=\"layoutArea\"><\/div>\n","protected":false},"excerpt":{"rendered":"<\/p>\n<p>Books and Book Chapters\n<\/p>\n<p>[B3] M.K. Islam, S. Aridhi, M. Smail-Tabbone. (2022). From Competition to Collaboration: Ensembling Similarity-Based Heuristics for Supervised Link Prediction in Biological Graphs. In: <em>Communications in Computer and Information Science<\/em>, vol 1550. Springer, Cham.<\/p>\n<p>[B2] M. Zoghlami, S. Aridhi, M. Maddouri and E. Mephu Nguifo. An Overview of in Silico Methods for the Prediction of Ionizing Radiation Resistance in Bacteria. In:\u00a0<em>Ionizing Radiation: Advances in Research and Applications<\/em>, <em>Physics Research and Technology Series<\/em>, \u00a0ISBN: 978-1-53613-539-8, 2018.<\/p>\n<p><a href=\"http:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2018\/03\/book.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-812 alignright\" src=\"http:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2018\/03\/book-202x300.jpg\" alt=\"\" width=\"124\" height=\"184\" srcset=\"https:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2018\/03\/book-202x300.jpg 202w, https:\/\/members.loria.fr\/SAridhi\/wp-content\/blogs.dir\/125\/files\/sites\/125\/2018\/03\/book.jpg 336w\" sizes=\"auto, (max-width: 124px) 100vw, 124px\" \/><\/a>[B1] S.<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-13","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/members.loria.fr\/SAridhi\/wp-json\/wp\/v2\/pages\/13","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/members.loria.fr\/SAridhi\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/members.loria.fr\/SAridhi\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/members.loria.fr\/SAridhi\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/members.loria.fr\/SAridhi\/wp-json\/wp\/v2\/comments?post=13"}],"version-history":[{"count":191,"href":"https:\/\/members.loria.fr\/SAridhi\/wp-json\/wp\/v2\/pages\/13\/revisions"}],"predecessor-version":[{"id":1202,"href":"https:\/\/members.loria.fr\/SAridhi\/wp-json\/wp\/v2\/pages\/13\/revisions\/1202"}],"wp:attachment":[{"href":"https:\/\/members.loria.fr\/SAridhi\/wp-json\/wp\/v2\/media?parent=13"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}