Projects per year
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
the Enron “who communicates with whom” social graph, 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 the structure
of real-world graphs.
Original language | English |
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Number of pages | 12 |
Publication status | Published - 27 Sept 2013 |
Event | Second International Workshop on New Frontiers in Mining Complex Patterns - Prague, Czech Republic Duration: 23 Sept 2013 → 27 Sept 2013 |
Conference
Conference | Second International Workshop on New Frontiers in Mining Complex Patterns |
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Country/Territory | Czech Republic |
City | Prague |
Period | 23/09/2013 → 27/09/2013 |
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Dive into the research topics of 'AGWAN: A Generative Model for Labelled, Weighted Graphs'. Together they form a unique fingerprint.Projects
- 1 Finished
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R1118ECI: Centre for Secure Information Technologies (CSIT)
McCanny, J. V. (PI), Cowan, C. (CoI), Crookes, D. (CoI), Fusco, V. (CoI), Linton, D. (CoI), Liu, W. (CoI), Miller, P. (CoI), O'Neill, M. (CoI), Scanlon, W. (CoI) & Sezer, S. (CoI)
01/08/2009 → 30/06/2014
Project: Research