Begin adding more

principal.tex

@@ -51,9 +51,12 @@
 \begin{frame}{Analysis methods for a network}
     TODO (Supprimable) Several methods~:
     \begin{itemize}
-        \item Metrics~: degree, centrality, nesting \dots
+        \item Metrics at node level: degree, centrality\dots
-        \item Network embedding with GNN
+        \item Metrics at a network level: density, nestedness\dots
-        \item \textbf<2>{\emph{Clustering} of nodes with latent variable models}
+        \item \textbf<2>{Node embedding and/or clustering with latent variable models
+                  \\\cite{hoffLatentSpaceApproaches2002,snijdersEstimationPredictionStochastic1997}}
+        \item Node or network embedding with Graph Convolutional Networks
+              \\\cite{kipfVariationalGraphAutoEncoders2016a}
     \end{itemize}
 \end{frame}
 
@@ -289,6 +292,19 @@
 
 \begin{frame}
     TODO Interesting structures detected, functional roles in the british networks.
+    \begin{figure}
+        \begin{subfigure}[t]{0.5\textwidth}
+            \centering
+            \includegraphics[width=0.35\textwidth]{img/baldock/bombus-hortorum.jpeg}
+            \caption{\emph{Bombus Hortorum} or garden bumblebee}
+        \end{subfigure}\hfil
+        \begin{subfigure}[t]{0.5\textwidth}
+            \centering
+            \includegraphics[width=0.35\textwidth]{img/baldock/bombus-lapidarius.jpeg}
+            \caption{\emph{Bombus Lapidarius} or red-tailed bumblebee}
+        \end{subfigure}
+    \end{figure}
+
 \end{frame}
 \begin{frame}
     \frametitle{Network clustering}

references.bib

@@ -545,6 +545,24 @@ Read\_Status\_Date: 2025-05-14T20:18:00.025Z},
   file = {/home/louis/snap/zotero-snap/common/Zotero/storage/PPHP33Z9/Govaert et Nadif - 2010 - Latent Block Model for Contingency Table.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/UT8TARCX/govaert2010.pdf.pdf}
 }
 
+@article{hoffLatentSpaceApproaches2002,
+  title = {Latent {{Space Approaches}} to {{Social Network Analysis}}},
+  author = {Hoff, Peter D and Raftery, Adrian E and Handcock, Mark S},
+  date = {2002-12-01},
+  journaltitle = {Journal of the American Statistical Association},
+  volume = {97},
+  number = {460},
+  pages = {1090--1098},
+  publisher = {Taylor \& Francis},
+  issn = {0162-1459},
+  doi = {10.1198/016214502388618906},
+  url = {https://doi.org/10.1198/016214502388618906},
+  urldate = {2024-05-20},
+  abstract = {Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space.” We make inference for the social space within maximum likelihood and Bayesian frameworks, and propose Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates. We present analyses of three standard datasets from the social networks literature, and compare the method to an alternative stochastic blockmodeling approach. In addition to improving on model fit for these datasets, our method provides a visual and interpretable model-based spatial representation of social relationships and improves on existing methods by allowing the statistical uncertainty in the social space to be quantified and graphically represented.},
+  keywords = {Conditional independence model,Latent position model,Network data,Random graph,Visualization},
+  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/7UYRBBA2/Hoff et al. - 2002 - Latent Space Approaches to Social Network Analysis.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/R4TGSVGP/016214502388618906.pdf.pdf}
+}
+
 @article{hollandStochasticBlockmodelsFirst1983,
   title = {Stochastic Blockmodels: {{First}} Steps},
   shorttitle = {Stochastic Blockmodels},
@@ -672,6 +690,24 @@ Read\_Status\_Date: 2025-05-14T20:18:00.025Z},
   file = {/home/louis/snap/zotero-snap/common/Zotero/storage/W2RM4C9T/6771089.html}
 }
 
+@online{kipfVariationalGraphAutoEncoders2016a,
+  title = {Variational {{Graph Auto-Encoders}}},
+  author = {Kipf, Thomas N. and Welling, Max},
+  date = {2016-11-21},
+  eprint = {1611.07308},
+  eprinttype = {arXiv},
+  eprintclass = {stat},
+  doi = {10.48550/arXiv.1611.07308},
+  url = {http://arxiv.org/abs/1611.07308},
+  urldate = {2025-05-09},
+  abstract = {We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.},
+  pubstate = {prepublished},
+  keywords = {/unread,Computer Science - Machine Learning,Statistics - Machine Learning},
+  annotation = {Read\_Status: New\\
+Read\_Status\_Date: 2025-05-09T11:54:37.094Z},
+  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/5THEWLW6/Kipf et Welling - 2016 - Variational Graph Auto-Encoders.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/BBTHQNRZ/1611.html}
+}
+
 @online{kumpulainenYourBlockOur2024,
   title = {From Your {{Block}} to Our {{Block}}: {{How}} to {{Find Shared Structure}} between {{Stochastic Block Models}} over {{Multiple Graphs}}},
   shorttitle = {From Your {{Block}} to Our {{Block}}},