Learning Graph Representations by Dendrograms

2018/07/13 by Thomas Bonald, Bonald, Thomas, Bertrand Charpentier +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.1807.05087

openalex publication_date 2018/07/13 · openalex created_date 2018/08/03 · openalex updated_date 2026/07/28

Abstract

Hierarchical graph clustering is a common technique to reveal the multi-scale structure of complex networks. We propose a novel metric for assessing the quality of a hierarchical clustering. This metric reflects the ability to reconstruct the graph from the dendrogram, which encodes the hierarchy. The optimal representation of the graph defines a class of reducible linkages leading to regular dendrograms by greedy agglomerative clustering.

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