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Labelled Graph Rewriting Meets Social Networks

  • Maribel Fernández
  • Hélène Kirchner
  • Bruno Pinaud
  • Jason Vallet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9942)

Abstract

The intense development of computing techniques and the increasing volumes of produced data raise many modelling and analysis challenges. There is a need to represent and analyse information that is: complex –due to the presence of massive and highly heterogeneous data–, dynamic –due to interactions, time, external and internal evolutions–, connected and distributed in networks. We argue in this work that relevant concepts to address these challenges are provided by three ingredients: labelled graphs to represent networks of data or objects; rewrite rules to deal with concurrent local transformations; strategies to express control versus autonomy and to focus on points of interests. To illustrate the use of these concepts, we choose to focus our interest on social networks analysis, and more precisely in this paper on random network generation. Labelled graph strategic rewriting provides a formalism in which different models can be generated and compared. Conversely, the study of social networks, with their size and complexity, stimulates the search for structure and efficiency in graph rewriting. It also motivated the design of new or more general kinds of graphs, rules and strategies (for instance, to define positions in graphs), which are illustrated here. This opens the way to further theoretical and practical questions for the rewriting community.

Keywords

Social Network Cluster Coefficient Label Graph Derivation Tree Locate Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Guy Melançon (University of Bordeaux) and all the other members of the Porgy project. We also thank the anonymous reviewer for carefully reading this paper and making valuable suggestions for improvement.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Maribel Fernández
    • 1
  • Hélène Kirchner
    • 2
  • Bruno Pinaud
    • 3
  • Jason Vallet
    • 3
  1. 1.King’s College LondonLondonUK
  2. 2.InriaRocquencourtFrance
  3. 3.CNRS UMR5800 LaBRIUniversity of BordeauxBordeauxFrance

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