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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no, viewport-fit=cover"
id="viewport-meta">
<title>MODiR: Joint Visualisation of Network and Text Data</title>
<style type="text/css">
body {
background-color: #41464d;
color: #fff;
font-family: "TabletGothic", "Helvetica Neue", Helvetica, Arial, FreeSans, sans-serif;
font-size: 1em;
line-height: 1.5;
margin: 0;
padding: 100px 0 0 0;
}
a {
color: #b91109;
text-decoration: none
}
a:hover, a:active, a:focus {
color: #ff8600
}
li {
list-style: none;
}
li::before {
content: '>';
margin-left: -1.3em;
margin-right: 0.3em;
}
main {
background-color: #ffffff;
box-shadow: 0 0 0.3125em rgba(65, 70, 77, 0.5);
color: #252525;
margin: 0 auto;
max-width: 62.5rem;
min-width: 300px;
padding: env(safe-area-inset-top) env(safe-area-inset-right) 0 env(safe-area-inset-left);
}
header {
padding: .625rem 1.25rem 0;
}
header a {
color: #252525;
}
#header-nav {
text-align: right;
color: #000;
text-transform: uppercase;
display: flex;
flex-flow: row wrap;
}
nav {
display: block;
}
nav.nav-left {
margin-right: auto;
}
nav.nav-main {
border-bottom: 1px solid #cccccf;
font-size: .875rem;
line-height: 2.25rem;
letter-spacing: 0.05em;
}
nav.nav-main a {
font-size: .875rem;
line-height: 2.25rem;
padding-right: .6875rem;
}
#header-nav a {
line-height: 2rem;
font-size: 0.625rem;
letter-spacing: 1px;
margin-right: 0.75rem;
}
.logo {
text-shadow: 1px 1px 0 #8D8D8D, 2px 2px 0 #8D8D8D, 3px 3px 0 #8D8D8D, 4px 4px 0 #8D8D8D, 5px 5px 0 #8D8D8D, 6px 6px 0 #8D8D8D; /* , 7px 7px 0 #8D8D8D, 8px 8px 0 #8D8D8D, 9px 9px 0 #8D8D8D, 10px 10px 5px rgba(0, 0, 0, 0.8);*/
font-size: 3rem;
font-weight: bold;
color: white;
text-decoration: underline;
padding: 0;
margin: 0;
height: 5.375rem;
}
section {
position: relative;
}
section.fat-border {
margin-top: 2.5rem;
margin-left: 1.25rem;
margin-right: 1.25rem;
padding-left: 1.25rem;
padding-right: 1.25rem;
color: #252525;
position: relative;
display: block;
}
section.fat-border > div {
padding-left: 1.25rem;
padding-right: 1.25rem;
border: 6px solid #eeeef0;
margin: 1.5rem auto 1.25rem;
}
section.fat-border > div > h1 {
top: -1rem;
margin: 0 auto;
position: relative;
padding: 0;
text-align: center;
font-size: 1em;
}
section.fat-border > div > h1 > span {
background-color: white;
display: inline-block;
padding: 0 .75rem;
font-size: 0.875rem;
letter-spacing: 0.125em;
font-weight: normal;
max-width: 90%;
text-transform: uppercase;
text-align: center;
}
section > div > h2 {
font-size: 1.375rem;
margin: 0 0 0.4em 0;
letter-spacing: .03em;
line-height: 1.3;
font-weight: 700;
}
section > div > p {
font-size: 1rem;
margin: 0;
letter-spacing: .03em;
padding-bottom: 1.1rem;
}
section.dark, section.light {
margin-top: 2.5rem;
color: #252525;
position: relative;
display: block;
}
section.dark > div, section.light > div {
margin-left: 2.5rem;
margin-right: 2.5rem;
margin-top: 1.25rem;
padding-top: 1.25rem;
padding-left: 1.25rem;
padding-right: 1.25rem;
max-width: 55rem;
}
section.dark > h1 > a, section.light > h1 > a {
color: inherit;
font-weight: bold;
position: absolute;
right: 1em;
}
section.dark > h1 > a > svg,
section.light > h1 > a > svg {
transform: rotate(-90deg);
fill: currentColor;
}
section.dark > h1, section.light > h1 {
padding: .9375rem 1.25rem;
line-height: 1.875rem;
letter-spacing: .03em;
font-size: 1.375rem;
background-color: #33333c;
color: #fff;
display: flex;
text-rendering: optimizeLegibility;
}
section.light > h1 {
background-color: #eeeef0;
color: #252525;
}
#sect-publications li {
margin-bottom: 0.375em;
}
a.small {
font-size: 0.8em;
}
#sect-examples div {
display: flex;
flex-grow: 1;
flex-basis: 0;
justify-content: center;
margin: 0;
padding: 0;
max-width: none;
}
#sect-examples figure {
margin: 0 0 0 1.5rem;
padding: 0;
text-align: center;
position: relative;
overflow: hidden;
max-height: 10rem;
flex: 1;
}
#sect-examples figure:last-child {
margin-right: 1.5rem;
}
#sect-examples figure > img {
width: 100%;
margin: 0;
padding: 0;
position: relative;
transition: all 0.3s cubic-bezier(.17, .67, .63, .99);
display: block;
height: auto !important;
}
#sect-examples figure:hover {
/*overflow: visible;*/
}
#sect-examples figure:hover > img {
transform: scale(1.2);
filter: brightness(35%);
}
#sect-examples figure:hover a,
#sect-examples figure:hover figcaption {
opacity: 1;
}
#sect-examples figure figcaption {
position: absolute;
margin: 0;
padding: 0;
top: 0;
left: 50%;
transform: translate(-50%, 0);
color: #eeeef0;
font-size: 1.1em;
letter-spacing: 0.1em;
width: 100%;
opacity: 0;
}
#sect-examples figure a {
position: absolute;
bottom: 0.5em;
left: 50%;
transform: translate(-50%, 0);
text-transform: uppercase;
color: #fff;
font-weight: bold;
border: 1px solid #fff;
transition: all 0.4s ease;
margin: 0;
padding: 0.3em 0.5em;
opacity: 0;
}
#sect-examples figure a:hover {
box-shadow: 0 0 5px #fff;
}
.explore-button {
display: inline-block;
background-color: #b91109;
border-radius: 5px;
color: #fff;
font-size: .8125rem;
font-weight: bold;
letter-spacing: .08em;
line-height: 1.125rem;
margin: 0;
padding: .625rem 1rem;
text-align: center;
text-transform: uppercase;
transition: all 0.15s ease-in-out;
text-rendering: optimizeLegibility;
position: absolute;
right: 3em;
bottom: 1.3rem;
}
.explore-button:hover {
background-color: #880d07;
color: #fff;
cursor: pointer;
}
footer {
font-size: .875rem;
margin-top: 2.75rem;
text-align: center;
}
footer > div.foot-sep {
background-color: #33333c;
background-image: linear-gradient(to right, #4A4A4A 0%, #2C2A2B 30%, #494A4E 60%, #3B3B3D 80%, #4A4A4A 100%);
padding: 3.5rem 0;
display: block;
margin: 1.25rem 0 3.75rem 0;
/*border-bottom: 1px solid #cccccf;
margin: .625rem 1.25rem 3.75rem 1.25rem;*/
}
footer > div.linkdump {
background-color: #eeeef0;
padding-bottom: 1.25rem;
padding-top: 1.25rem;
font-weight: bold;
}
footer > div.linkdump a {
color: #252525;
margin: 0 .4em;
font-size: 0.875rem;
}
footer > div.linkdump a:hover,
footer > div.linkdump a:active,
footer > div.linkdump a:focus {
color: #000;
}
footer > div.foot {
background-color: #252525;
padding-bottom: 1.25rem;
padding-top: 1.25rem;
border: none;
color: #cccccf;
}
footer > div.foot > div {
text-align: center;
padding-bottom: 3.5rem;
padding-top: 0.75rem;
}
footer > div.foot svg g {
fill: #a1a1a3;
}
footer > div.foot:hover svg g,
footer > div.foot:active svg g,
footer > div.foot:focus svg g {
fill: #fff;
}
footer > div.foot a {
color: #a1a1a3;
}
footer > div.foot a:hover,
footer > div.foot a:active,
footer > div.foot a:focus {
color: #fff;
}
</style>
</head>
<body>
<main>
<header>
<div id="header-nav">
<nav class="nav-left">
<a href="https://github.com/TimRepke/modir-viewer">
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Frontend
</a>
<a href="https://github.com/HPI-Information-Systems/modir">
<svg style="height:1rem; margin-bottom: -.2rem" viewBox="0 0 256 250" version="1.1"
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MODiR
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MODiR
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<a href="#sect-examples">Examples</a>
<a href="#sect-about">About Us</a>
<a href="#sect-algorithms">Algorithm</a>
<a href="#sect-publications">Publications</a>
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<img style="padding: 1rem; margin: 1.5rem auto; width: 90%;display: block" src="img/banner.png" alt="" />
<section class="fat-border" id="sect-explore">
<div>
<h1><span>Joint Visualisation of Network and Text Data</span></h1>
<div style="display: flex">
<!--div style="flex-basis: 20rem">
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<div style="margin: 0 0 0 2rem">
<h2 style="margin-top:0">What is MODiR?</h2>
<p>
Many large text collections exhibit graph structures, either inherent to the content itself or
encoded in the metadata of the individual documents. Example graphs extracted from document
collections are co-author networks, citation networks, or named-entity-cooccurrence networks.
Furthermore, social networks can be extracted from email corpora, tweets, or social media. When
it comes to visualising these large corpora, either the textual content or the network graph are
used.
</p>
<p>
With MODiR (multi-objective dimensionality reduction), we propose to incorporate both, text and
graph, to not only visualise the semantic information encoded in the documents' content but also
the relationships expressed by the inherent network structure. To this end, we introduced a
novel algorithm based on multi-objective optimisation to jointly position embedded documents and
graph nodes in a two-dimensional landscape.
</p>
<!--a href="" class="explore-button">Explore</a-->
</div>
</div>
</div>
</section>
<section class="dark" id="sect-examples">
<h1>Examples <a href="#top">
<svg xmlns="http://www.w3.org/2000/svg" width="9" height="16" viewBox="0 0 9 16"
class="svg-symbol headed-meta__arrow" preserveAspectRatio="xMinYMin meet" role="img" aria-label="Icon">
<path d="M.287.461L.332.414a1.006 1.006 0 011.457 0L9 8l-7.211 7.586a1.006 1.006 0 01-1.421.036l-.036-.036-.045-.047a1.045 1.045 0 010-1.439L6.086 8 .287 1.9a1.045 1.045 0 010-1.439z"></path>
</svg>
</a></h1>
<div>
<figure>
<img src="img/demo_modir.png" alt="Small set of selected scientific publications" />
<figcaption>Small set of selected scientific publications</figcaption>
<a href="vis/index_modir.html">Explore</a>
</figure>
<figure>
<img src="img/demo_mcc.png" alt="WOS articles on climate change" />
<figcaption>WOS articles on climate change</figcaption>
<a href="vis/index_mcc.html">Explore</a>
</figure>
<figure>
<img src="img/demo_papers.png" alt="Semantic Scholar abstracts by four communities" />
<figcaption>Semantic Scholar abstracts by four communities</figcaption>
<a href="vis/index_papers.html">Explore</a>
</figure>
<figure>
<img src="img/demo_news.png" alt="News articles on Commerzbank" />
<figcaption>News articles on Commerzbank</figcaption>
<a href="vis/index_news.html">Explore</a>
</figure>
</div>
<ul style="margin: 2rem 4rem 2rem 1rem">
<li>
<strong>Example 1</strong> consists of 20 hand-picked top researchers from the fields of NLP, machine
learning, and databases including their co-authors and publications (abstracts) as found in <a
href="https://www.semanticscholar.org/">SemanticScholar</a>.
</li>
<li>
<strong>Example 2</strong> is based on articles in Web-of-Science that contain the keywords "Germany"
and "sustainab*" and the co-authorship network. This dataset is regularly used in the development of ML
models by the Mercator Institute for Climate Change Research.
</li>
<li>
<strong>Example 3</strong> is an extended dataset of Example 1. Based on that seed set, additional
papers are added based on co-authorships. This is best viewed with edge bundling activated to reduce
visual clutter; however, the bundling happens in the browser and might be slow to load. You can try it
<a href="vis/index_papers_bundle.html">[here]</a>.
</li>
<li>
<strong>Example 4</strong> contains business news from Bloomberg and Reuters newstickers that contain
"Commerzbank". There is also a <a href="vis/network.html">raw <strong>network visualisation</strong></a>
and word clouds <a class="small" href="vis/img/wordcloud_idf_top.png">[1]</a> <a class="small"
href="vis/img/wordcloud_wordfreq.png">[2]</a>
<a class="small" href="vis/img/wordcloud_tfidf_top.png">[3]</a> <a class="small"
href="vis/img/wordcloud_tfidf_mid.png">[4]</a>
of that data. Nodes and edges are extracted named entities and their co-occurrence in documents.
</li>
</ul>
</section>
<section class="dark" id="sect-about">
<h1>About the Project <a href="#top">
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</svg>
</a></h1>
<div>
<!--p>
test text<br>
more test text
</p-->
<h2>People</h2>
<ul>
<li>Tim Repke, <i>PhD Student</i> <a class="small" href="https://hpi.de/naumann/people/tim-repke.html">[contact]</a>
</li>
<li>Ben Hurdelhey, <i>Student Assistent</i></li>
<li>Alyona Vyshnevska, <i>Student Assistent</i></li>
<li>Ralf Krestel, <i>Senior Researcher</i> <a class="small"
href="https://hpi.de/naumann/sites/krestel/">[contact]</a>
</li>
</ul>
</div>
</section>
<section class="light" id="sect-algorithms">
<h1>How it works <a href="#top">
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<path d="M.287.461L.332.414a1.006 1.006 0 011.457 0L9 8l-7.211 7.586a1.006 1.006 0 01-1.421.036l-.036-.036-.045-.047a1.045 1.045 0 010-1.439L6.086 8 .287 1.9a1.045 1.045 0 010-1.439z"></path>
</svg>
</a></h1>
<div>
<h2>Input Data</h2>
<p>
MODiR is a dimensionality reduction algorithm that incorporates both semantic information and
network information.
It uses a hypergraph as an input, whereas nodes and edges are as follows:
</p>
<ul style="margin-top:0; ">
<li>Documents have <strong>semantic similarity</strong> (e.g. provided by euclidean distance between
embedding vectors)
</li>
<li><strong>Nodes</strong> are connected <strong>to documents</strong> (e.g. entities extracted from
text, authors of documents)
</li>
<li><strong>Nodes</strong> are connected to <strong>one another</strong> (e.g. co-occurring entities,
co-authorships,...)
</li>
</ul>
<h2>Multi-objective reduction</h2>
<p>
Our multi-objective dimensionality reduction algorithm MODiR jointly optimises the following three
objectives:
</p>
<ul style="margin-top:0; ">
<li>Similar documents are near one another</li>
<li>Dissimilar documents are apart from one another</li>
<li>Connected entities are near one another and their documents</li>
</ul>
<p>
For more details, please read our JCDL paper.
</p>
<h2>Interface</h2>
<p>
The result of MODiR are positions for documents and nodes.
As seen in the examples above, we built a simple interface to explore the data by keywords, categories,
or entities.
A heatmap is applied to the currently selected documents for readability.
The keywords/keyphrases are not part of this work, thus it is based on a very naive approach.
The canvas is divided into a grid, all documents per cell form a meta-document, of which the top tf-idf
ranked words are selected and randomly placed on the canvas.
</p>
</div>
</section>
<section class="light" id="sect-publications">
<h1>Publications <a href="#top">
<svg xmlns="http://www.w3.org/2000/svg" width="9" height="16" viewBox="0 0 9 16"
class="svg-symbol headed-meta__arrow" preserveAspectRatio="xMinYMin meet" role="img" aria-label="Icon">
<path d="M.287.461L.332.414a1.006 1.006 0 011.457 0L9 8l-7.211 7.586a1.006 1.006 0 01-1.421.036l-.036-.036-.045-.047a1.045 1.045 0 010-1.439L6.086 8 .287 1.9a1.045 1.045 0 010-1.439z"></path>
</svg>
</a></h1>
<div>
<ul>
<li>
Tim Repke and Ralf Krestel <i>(JCDL 2020, full paper)</i><br />
<strong>Visualising Large Document Collections By Jointly Modeling Text And Network
Structure</strong>.<br />
<a class="small" href="resources/jcdl2020.pdf">[PDF]</a> <a class="small"
href="https://2020.jcdl.org/">[JCDL'20]</a>
</li>
<li>
Tim Repke and Ralf Krestel <i>(IUI 2020, demo paper)</i><br />
Exploration Interface For Jointly Visualised Text And Graph Data.<br />
<a class="small" href="resources/iui2020_demo.pdf">[PDF]</a> <a class="small"
href="https://iui.acm.org/2020/">[IUI'20]</a>
</li>
<li>
Tim Repke and Ralf Krestel <i>(BigVis@EDBT/ICDT 2018, vision paper)</i><br />
Topic-Aware Network Visualisation To Explore Large Email Corpora.<br />
<a class="small" href="resources/BigVis_2018_paper_2.pdf">[PDF]</a> <a class="small"
href="http://bigvis2018.imis.athena-innovation.gr/">[BigVis'18]</a>
</li>
</ul>
</div>
</section>
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