Agwan: A Generative Model for Labelled, Weighted Graphs View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

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

ABSTRACT

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. More... »

PAGES

181-200

References to SciGraph publications

Book

TITLE

New Frontiers in Mining Complex Patterns

ISBN

978-3-319-08406-0
978-3-319-08407-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-08407-7_12

DOI

http://dx.doi.org/10.1007/978-3-319-08407-7_12

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031869609


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