Mining a medieval social network by kernel SOM and related methods

Abstract

This paper briefly presents several ways to understand the organization of a large social network (several hundreds of persons). We compare approaches coming from data mining for clustering the vertices of a graph (spectral clustering, self-organizing algorithms. . .) and provide methods for representing the graph from these analysis. All these methods are illustrated on a medieval social network and the way they can help to understand its organization is underlined.

References in corpus (3)

• Finding and evaluating community structure in networks
• Mixing patterns in networks
• Partitionnement d'un réseau de sociabilité à fort coefficient de clustering

Cited by in corpus (4)

• The random subgraph model for the analysis of an ecclesiastical network in Merovingian Gaul
• Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach
• Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks
• Goodness of fit of logistic models for random graphs