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Author Nettleton, David F.
Source SpringerLink
Content type Text
Publisher Springer Vienna
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Graphs and networks ♦ Online social networks ♦ Synthetic data generation ♦ Topology ♦ Attributes ♦ Attribute-values ♦ Seeds ♦ Communities ♦ Data Mining and Knowledge Discovery ♦ Applications of Graph Theory and Complex Networks ♦ Game Theory, Economics, Social and Behav. Sciences ♦ Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law ♦ Methodology of the Social Sciences
Abstract Two of the difficulties for data analysts of online social networks are (1) the public availability of data and (2) respecting the privacy of the users. One possible solution to both of these problems is to use synthetically generated data. However, this presents a series of challenges related to generating a realistic dataset in terms of topologies, attribute values, communities, data distributions, correlations and so on. In the following work, we present and validate an approach for populating a graph topology with synthetic data which approximates an online social network. The empirical tests confirm that our approach generates a dataset which is both diverse and with a good fit to the target requirements, with a realistic modeling of noise and fitting to communities. A good match is obtained between the generated data and the target profiles and distributions, which is competitive with other state of the art methods. The data generator is also highly configurable, with a sophisticated control parameter set for different “similarity/diversity” levels.
ISSN 18695450
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-07-01
Publisher Place Vienna
e-ISSN 18695469
Journal Social Network Analysis and Mining
Volume Number 6
Issue Number 1
Page Count 33
Starting Page 1
Ending Page 33


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Source: SpringerLink