AGWAN: a generative model for labelled, weighted graphs

Michael Davis, Weiru Liu, Paul Miller, Ruth F. Hunter, Frank Kee

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
230 Downloads (Pure)


Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we present Agwan (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the Agwan model to real-world graphs and for generating random graphs from the model. Using real-world directed and undirected graphs as input, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to graph structure.
Original languageEnglish
Title of host publicationNew Frontiers in Mining Complex Patterns
Subtitle of host publicationSecond International Workshop, NFMCP 2013, Held in Conjunction with ECML/PKDD 2013, Prague, Czech Republic, September 27, 2013: Revised Selected Papers
Number of pages20
ISBN (Electronic)9783319084077
ISBN (Print)9783319084060
Publication statusPublished - 25 Mar 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer International Publishing
ISSN (Print)0302-9743

Structured keywords

  • Jean Golding


  • Graph generators
  • Graph mining
  • Weighted graphs
  • Labelled graphs, Network models
  • Random graphs

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