Chapter

From Social Data Mining and Analysis to Prediction and Community Detection

Part of the series Lecture Notes in Social Networks pp 141-177

Date:

A LexDFS-Based Approach on Finding Compact Communities

  • Jean CreusefondAffiliated withNormandy University Email author 
  • , Thomas LargillierAffiliated withNormandy University
  • , Sylvain PeyronnetAffiliated withix-labs

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Abstract

This article presents an efficient hierarchical clustering algorithm based on a graph traversal algorithm called LexDFS. This traversal algorithm has the property of going through the clustered parts of the graph in a small number of iterations, making them recognisable. The time complexity of our method is in O(n × log(n)). It is simple to implement and a thorough study shows that it outputs clusterings that are closer to some ground-truths than its competitors. Experiments are also carried out to analyse the behaviour of the algorithm during execution on sample graphs. This article also features a quality function called compactness, which measures how efficient is the cluster for internal communications. We prove that this quality function features interesting theoretical properties.

Keywords

Community detection Compactness LexDFS