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QGraph: A Quality Assessment Index for Graph Clustering

  • Maria HalkidiEmail author
  • Iordanis Koutsopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)

Abstract

In this work, we aim to study the cluster validity problem for graph data. We present a new validity index that evaluates structural characteristics of graphs in order to select the clusters that best represent the communities in a graph. Since the work of defining what constitutes cluster in a graph is rather difficult, we exploit concepts of graph theory in order to evaluate the cohesiveness and separation of nodes. More specifically, we use the concept of degeneracy, and graph density to evaluate the connectivity of nodes in and between clusters. The effectiveness of our approach is experimentally evaluated using real-world data collections.

Keywords

Cluster validity Graph clustering Data analysis 

Notes

Acknowledgment

This work has been partly supported by the University of Piraeus Research Center. I. Koutsopoulos acknowledges the support from the AUEB internal project “Original scientific publications”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of PiraeusPiraeusGreece
  2. 2.Athens University of Economics and BusinessAthensGreece

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