{"id":616,"date":"2025-05-18T21:27:52","date_gmt":"2025-05-18T21:27:52","guid":{"rendered":"https:\/\/conferences.css-fr.org\/?page_id=616"},"modified":"2025-06-24T10:02:07","modified_gmt":"2025-06-24T10:02:07","slug":"parallel-session-3","status":"publish","type":"page","link":"https:\/\/conferences.css-fr.org\/?page_id=616","title":{"rendered":"Parallel Session 3"},"content":{"rendered":"<div id='full_slider_1'  class='avia-fullwidth-slider main_color avia-shadow   avia-builder-el-0  el_before_av_one_full  avia-builder-el-first   container_wrap fullsize'  ><div  class='avia-slideshow av-mau5yr9z-ee29b9fd8219af1bb7c0797e663555d0 avia-slideshow-featured av_slideshow_full avia-slide-slider av-slideshow-ui av-control-default av-slideshow-manual av-loop-once av-loop-manual-endless av-default-height-applied   avia-slideshow-1' data-slideshow-options=\"{&quot;animation&quot;:&quot;slide&quot;,&quot;autoplay&quot;:false,&quot;loop_autoplay&quot;:&quot;once&quot;,&quot;interval&quot;:5,&quot;loop_manual&quot;:&quot;manual-endless&quot;,&quot;autoplay_stopper&quot;:false,&quot;noNavigation&quot;:false,&quot;bg_slider&quot;:false,&quot;keep_padding&quot;:false,&quot;hoverpause&quot;:false,&quot;show_slide_delay&quot;:0}\"  itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><ul class='avia-slideshow-inner ' style='padding-bottom: 28.666666666667%;'><li  class='avia-slideshow-slide av-mau5yr9z-ee29b9fd8219af1bb7c0797e663555d0__0  av-single-slide slide-1 slide-odd'><div data-rel='slideshow-1' class='avia-slide-wrap '   ><div class='av-slideshow-caption av-mau5yr9z-ee29b9fd8219af1bb7c0797e663555d0__0 caption_fullwidth caption_bottom'><div class=\"container caption_container\"><div class=\"slideshow_caption\"><div class=\"slideshow_inner_caption\"><div class=\"slideshow_align_caption\"><h2 class='avia-caption-title '  itemprop=\"name\" >Parallel Session 3 - Tuesday 24th 11:50-12:50<\/h2><div class='avia-caption-content '  itemprop=\"description\" ><p>Social systems 2 \/ Complex method 2<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><img decoding=\"async\" fetchpriority=\"high\" class=\"wp-image-376 avia-img-lazy-loading-not-376\"  src=\"https:\/\/conferences.css-fr.org\/wp-content\/uploads\/2024\/11\/ville-1500x430.png\" width=\"1500\" height=\"430\" title='ville' alt=''  itemprop=\"thumbnailUrl\" srcset=\"https:\/\/conferences.css-fr.org\/wp-content\/uploads\/2024\/11\/ville-1500x430.png 1500w, https:\/\/conferences.css-fr.org\/wp-content\/uploads\/2024\/11\/ville-300x87.png 300w\" sizes=\"(max-width: 1500px) 100vw, 1500px\" \/><\/div><\/li><\/ul><\/div><\/div><div id='after_full_slider_1'  class='main_color av_default_container_wrap container_wrap fullsize'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-616'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-cjqtk-8adaa6dabe228aa3e66701b88f15264d av_one_full  avia-builder-el-1  el_after_av_slideshow_full  avia-builder-el-no-sibling  first flex_column_div  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-mau6028o-9f5e7a85575a7ac0de12b22858aad388\">\n#top .av-special-heading.av-mau6028o-9f5e7a85575a7ac0de12b22858aad388{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-mau6028o-9f5e7a85575a7ac0de12b22858aad388 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-mau6028o-9f5e7a85575a7ac0de12b22858aad388 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-mau6028o-9f5e7a85575a7ac0de12b22858aad388 av-special-heading-h3  avia-builder-el-2  el_before_av_textblock  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Complex Methods 2<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><br \/>\n<section  class='av_textblock_section av-mau608no-c8302a179be161126b3bb8b67b0182fe '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><p>Auditorium<\/p>\n<p><strong>Chair:<\/strong> Annick Vignes<\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-mau61lp7-1381641ed32f9c2a3441fcc7000e3a77  avia-builder-el-4  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-mau60s6u-01a7740d09c562d48c566c3dd42880c9'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-1' data-fake-id='#toggle-id-1' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-1' data-slide-speed=\"200\" data-title=\"Comparing networks through their node dynamics, H\u00e9lo\u00efse Pr\u00e9vot, Sergio Magalh\u00e3es Contente, Alexis B\u00e9nichou and Christian Vestergaard\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Comparing networks through their node dynamics, H\u00e9lo\u00efse Pr\u00e9vot, Sergio Magalh\u00e3es Contente, Alexis B\u00e9nichou and Christian Vestergaard\" data-aria_expanded=\"Click to collapse: Comparing networks through their node dynamics, H\u00e9lo\u00efse Pr\u00e9vot, Sergio Magalh\u00e3es Contente, Alexis B\u00e9nichou and Christian Vestergaard\">Comparing networks through their node dynamics, H\u00e9lo\u00efse Pr\u00e9vot, Sergio Magalh\u00e3es Contente, Alexis B\u00e9nichou and Christian Vestergaard<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-1' aria-labelledby='toggle-toggle-id-1' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>The functions of biological networks are often hypothesized to be governed by an ensemble of regularly repeated, small subgraphs termed motifs. Network theory provides statistical tools to detect potential candidate motifs that may govern the functions of a network. However, these methods are grounded in random graph models which purely describe the network topology, independently of the networks\u2019 dynamics and function. In parallel, there exists a multitude of dynamic processes on networks, whose evolution imply dynamical representations that we generally apply to make sense of, or simply guess, the computations (i.e., the functions) occurring in biological motifs. Consequently, there is a methodological gap between how we infer motifs\u2014with random graph models\u2014and how we study them\u2014with dynamical models. Furthermore, existing topological network similarity measures only provide one-dimensional representations of the difference between a motif pair, and thus cannot capture all relevant differences. Thus, we argue that biological network motifs are best compared not directly by their topology but by how a biologically relevant dynamic process unfolds on them.<\/p>\n<p>Here, we report on our progress in designing dynamics-based dissimilarity measures between candidate network motifs, formally defined as subgraph isomorphism classes called graphlets. We formally introduce the concept of node-dynamics-based dissimilarity between graphs, and we investigate the subtleties involved in defining appropriate distances between individual node dynamics and aggregating them to compare labeled and unlabeled graphs. Given its pertinence in biological applications as well as its well-established characterization in the mathematics and physics literature, we take the Kuramoto model as a working example and propose specific dissimilarity measures based on node synchronization, which we use to compare and cluster directed graphlets of up to five nodes. We discuss how to design dissimilarities for graphlets with and without labeled nodes (which requires accounting for their automorphism and isomorphism groups, respectively) and how this leads to different perceptions of proximity between graphlets. And we also show how dissimilarity measures based on non-linear synchronization dynamics differ from measures based on linear dynamics and from purely topological distance measures.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau612yv-2cee57af56b33f4b2ed815f67a95f94f'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-2' data-fake-id='#toggle-id-2' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-2' data-slide-speed=\"200\" data-title=\"Generative kernel embedding for weighted directed networks, Alex Barbier-Chebbah\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Generative kernel embedding for weighted directed networks, Alex Barbier-Chebbah\" data-aria_expanded=\"Click to collapse: Generative kernel embedding for weighted directed networks, Alex Barbier-Chebbah\">Generative kernel embedding for weighted directed networks, Alex Barbier-Chebbah<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-2' aria-labelledby='toggle-toggle-id-2' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><h3>Alex Barbier-Chebbah, Nicolas Billy, Jean-Baptiste Masson, Srinivas Turaga and Christian Vestergaard<\/h3>\n<blockquote>\n<p>Network data are inherently high-dimensional, so identifying functional roles of nodes and subgraphs in a whole biological network by exhaustive screening is in general unfeasible. Thus, in order to characterize their peculiar structural features and understand their function, it is crucial to extract relevant low-dimensional representations of these. Network embedding provides a powerful statistical framework for analyzing network data, and in particular biological networks, naturally accounting for their nodes\u2019 real space or latent structures and tendency to cluster in functional groups. However, including the heterogeneous, asymmetrical, and weighted characteristics of many biological networks in a latent space model remains a significant challenge.<\/p>\n<p>Building on recent tools, we here propose an embedding approach specifically tailored to model these structural constraints. Our model provides a tractable likelihood for the edge weights between each pair of nodes based on a dual-space latent embedding with learnable distance kernels. This makes it possible to simultaneously: 1. account for edge weights and deterministic asymmetries in connectivity by<br \/>\nusing two different spaces for ingoing and outgoing connections; 2. learn the most appropriate distance kernels and embedding dimension from data, and extract a low-dimensional latent representation of a network; 3. identify specific latent space features to uncover hidden structural features; 4. generate artificial networks with realistic and tunable structural features, which may serve as null models and to<br \/>\ninvestigate the functional importance of identified latent features in simulations of network dynamics. We validate our model on synthetic networks and apply it to characterize the complete adult Drosophila<br \/>\nconnectome.<\/p>\n<\/blockquote>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau61k72-d23423a43e5d176af544d396e47de4cc'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-3' data-fake-id='#toggle-id-3' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-3' data-slide-speed=\"200\" data-title=\"Interpretable Early Warnings using Machine Learning in an Online Game-experiment, Guillaume Falmagne\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Interpretable Early Warnings using Machine Learning in an Online Game-experiment, Guillaume Falmagne\" data-aria_expanded=\"Click to collapse: Interpretable Early Warnings using Machine Learning in an Online Game-experiment, Guillaume Falmagne\">Interpretable Early Warnings using Machine Learning in an Online Game-experiment, Guillaume Falmagne<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-3' aria-labelledby='toggle-toggle-id-3' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-616","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages\/616","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=616"}],"version-history":[{"count":12,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages\/616\/revisions"}],"predecessor-version":[{"id":874,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages\/616\/revisions\/874"}],"wp:attachment":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=616"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}