You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: _data/publications.yml
+16Lines changed: 16 additions & 0 deletions
Original file line number
Diff line number
Diff line change
@@ -1,3 +1,19 @@
1
+
- title: "Interpreting Temporal Graph Neural Networks with Koopman Theory"
2
+
authors: "Michele Guerra, Simone Scardapane, Filippo Maria Bianchi"
3
+
figure: "figs/publications/koopman.png"
4
+
abstract: "We propose an XAI technique based on Koopman theory to interpret temporal graphs and the spatio-temporal Graph Neural Newtworks used to process them. The proposed approach allows to identify nodes and time steps when relevant events occur."
<?xml version="1.0" encoding="utf-8"?><feedxmlns="http://www.w3.org/2005/Atom" ><generatoruri="https://jekyllrb.com/"version="4.3.4">Jekyll</generator><linkhref="http://localhost:4000/feed.xml"rel="self"type="application/atom+xml" /><linkhref="http://localhost:4000/"rel="alternate"type="text/html" /><updated>2024-10-20T12:21:21+02:00</updated><id>http://localhost:4000/feed.xml</id><titletype="html">Northernmost GraphML Group</title><subtitle>The Northermost GraphML group in the world, based in Tromsø, Norway.
1
+
<?xml version="1.0" encoding="utf-8"?><feedxmlns="http://www.w3.org/2005/Atom" ><generatoruri="https://jekyllrb.com/"version="4.3.4">Jekyll</generator><linkhref="http://localhost:4000/feed.xml"rel="self"type="application/atom+xml" /><linkhref="http://localhost:4000/"rel="alternate"type="text/html" /><updated>2024-10-20T12:58:42+02:00</updated><id>http://localhost:4000/feed.xml</id><titletype="html">Northernmost GraphML Group</title><subtitle>The Northermost GraphML group in the world, based in Tromsø, Norway.
Copy file name to clipboardExpand all lines: _site/publications.html
+60-4Lines changed: 60 additions & 4 deletions
Original file line number
Diff line number
Diff line change
@@ -53,6 +53,62 @@ <h1>Publications</h1>
53
53
54
54
<sectionclass="publications">
55
55
56
+
<divclass="publication">
57
+
58
+
<!-- Title -->
59
+
<h3class="pub-title"><strong>Interpreting Temporal Graph Neural Networks with Koopman Theory</strong></h3>
60
+
61
+
<!-- Authors -->
62
+
<pclass="authors">
63
+
<emstyle="color: gray;">Michele Guerra, Simone Scardapane, Filippo Maria Bianchi</em>
64
+
</p>
65
+
66
+
<!-- Venue (optional) -->
67
+
68
+
69
+
<!-- Figure -->
70
+
71
+
<divclass="pub-figure">
72
+
<imgsrc="figs/publications/koopman.png" alt="Figure for Interpreting Temporal Graph Neural Networks with Koopman Theory">
73
+
</div>
74
+
75
+
76
+
<!-- Abstract -->
77
+
<p>We propose an XAI technique based on Koopman theory to interpret temporal graphs and the spatio-temporal Graph Neural Newtworks used to process them. The proposed approach allows to identify nodes and time steps when relevant events occur.</p>
<metaproperty="twitter:title" content="Graph neural networks and Physics" />
25
25
<scripttype="application/ld+json">
26
-
{"@context":"https://schema.org","@type":"BlogPosting","dateModified":"2024-10-20T12:21:21+02:00","datePublished":"2024-10-20T12:21:21+02:00","description":"Graph neural networks and Physics","headline":"Graph neural networks and Physics","mainEntityOfPage":{"@type":"WebPage","@id":"http://localhost:4000/theses/gnn-physics/"},"publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"http://localhost:4000/figs/NGMLGlogo2.png"}},"url":"http://localhost:4000/theses/gnn-physics/"}</script>
26
+
{"@context":"https://schema.org","@type":"BlogPosting","dateModified":"2024-10-20T12:58:42+02:00","datePublished":"2024-10-20T12:58:42+02:00","description":"Graph neural networks and Physics","headline":"Graph neural networks and Physics","mainEntityOfPage":{"@type":"WebPage","@id":"http://localhost:4000/theses/gnn-physics/"},"publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"http://localhost:4000/figs/NGMLGlogo2.png"}},"url":"http://localhost:4000/theses/gnn-physics/"}</script>
<metaproperty="twitter:title" content="Clustering and graph coarsening with Graph Neural Networks" />
25
25
<scripttype="application/ld+json">
26
-
{"@context":"https://schema.org","@type":"BlogPosting","dateModified":"2024-10-20T12:21:21+02:00","datePublished":"2024-10-20T12:21:21+02:00","description":"Clustering and graph coarsening with Graph Neural Networks","headline":"Clustering and graph coarsening with Graph Neural Networks","mainEntityOfPage":{"@type":"WebPage","@id":"http://localhost:4000/theses/graph-pooling/"},"publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"http://localhost:4000/figs/NGMLGlogo2.png"}},"url":"http://localhost:4000/theses/graph-pooling/"}</script>
26
+
{"@context":"https://schema.org","@type":"BlogPosting","dateModified":"2024-10-20T12:58:42+02:00","datePublished":"2024-10-20T12:58:42+02:00","description":"Clustering and graph coarsening with Graph Neural Networks","headline":"Clustering and graph coarsening with Graph Neural Networks","mainEntityOfPage":{"@type":"WebPage","@id":"http://localhost:4000/theses/graph-pooling/"},"publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"http://localhost:4000/figs/NGMLGlogo2.png"}},"url":"http://localhost:4000/theses/graph-pooling/"}</script>
<metaproperty="twitter:title" content="Graph of text data, using large language models" />
25
25
<scripttype="application/ld+json">
26
-
{"@context":"https://schema.org","@type":"BlogPosting","dateModified":"2024-10-20T12:21:21+02:00","datePublished":"2024-10-20T12:21:21+02:00","description":"📝 Description The main idea is to build graphs of documents, graphs of topics contained in them, connecting abstract concepts and ideas inside the documents. We want to detect important news, automatically classify documents and get detailed information about a topic. The approach is in the same vein as Retrieval Augmented Generation RAG but we want to leverage the graph structure for a better retrieval of information.","headline":"Graph of text data, using large language models","mainEntityOfPage":{"@type":"WebPage","@id":"http://localhost:4000/theses/text-data/"},"publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"http://localhost:4000/figs/NGMLGlogo2.png"}},"url":"http://localhost:4000/theses/text-data/"}</script>
26
+
{"@context":"https://schema.org","@type":"BlogPosting","dateModified":"2024-10-20T12:58:42+02:00","datePublished":"2024-10-20T12:58:42+02:00","description":"📝 Description The main idea is to build graphs of documents, graphs of topics contained in them, connecting abstract concepts and ideas inside the documents. We want to detect important news, automatically classify documents and get detailed information about a topic. The approach is in the same vein as Retrieval Augmented Generation RAG but we want to leverage the graph structure for a better retrieval of information.","headline":"Graph of text data, using large language models","mainEntityOfPage":{"@type":"WebPage","@id":"http://localhost:4000/theses/text-data/"},"publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"http://localhost:4000/figs/NGMLGlogo2.png"}},"url":"http://localhost:4000/theses/text-data/"}</script>
0 commit comments