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Multiple dissimilarity SOM for clustering and visualizing graphs with node and edge attributes

Abstract : When wanting to understand the way a graph G is structured and how the relations it models organize groups of entities, clustering and visualization can be combined to provide the user with a global overview of the graph, on the form of a projected graph: a simplified graph is visualized in which the nodes correspond to a cluster of nodes in the original graph G (with a size proportional to the number of nodes that are classified inside this cluster) and the edges between two nodes have a width proportional to the number of links between the nodes of G classified in the two corresponding clusters. This approach can be trickier when additional attributes (numerical or factors) describe the nodes of G or when the edges of G are of different types and should be treated separately: the simplified representation should then represent similarities for all sets of information. In this proposal, we present a variant of Self-Organizing Maps (SOM), which is adapted to data described by one or several (dis)similarities or kernels recently published in (Olteanu & Villa-Vialaneix, 2015) and which is able to combine clustering and visualization for this kind of graphs.
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Contributor : Nathalie Vialaneix <>
Submitted on : Wednesday, July 15, 2015 - 10:31:06 PM
Last modification on : Tuesday, January 19, 2021 - 11:08:39 AM
Long-term archiving on: : Wednesday, April 26, 2017 - 2:41:07 AM


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  • HAL Id : hal-01175731, version 1
  • PRODINRA : 312304



Nathalie Vialaneix, Madalina Olteanu. Multiple dissimilarity SOM for clustering and visualizing graphs with node and edge attributes. International Conference on Machine Learning, Workshop FEAST, Jul 2015, Lille, France. 1p. ⟨hal-01175731⟩



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