Improving Individual Predictions using Social Networks Assortativity
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WSOM2017_dm_et_al.pdf
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530.77 KB
Format
Adobe PDF
Checksum (MD5)
01bc220eee2fee89167cbf60b5e471a7
Author(s)
Mulders, Dounia
de Bodt, Cyril
Bjelland, Johannes
Verleysen, Michel
Pentland, Alex Paul
de Montjoye, Yves-Alexandre
Date Issued
2017
Journal
12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Mulders, D. et al. "Improving individual predictions using social
networks assortativity" Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data
Visualization (WSOM+), June 2017, France, Institute of Electrical and Electronics Engineers (IEEE), 2017
Version
Author's final manuscript
Abstract
Social networks are known to be assortative with
respect to many attributes, such as age, weight, wealth, level
of education, ethnicity and gender. This can be explained by
influences and homophilies. Independently of its origin, this
assortativity gives us information about each node given its
neighbors. Assortativity can thus be used to improve individual
predictions in a broad range of situations, when data are missing
or inaccurate. This paper presents a general framework based on
probabilistic graphical models to exploit social network structures
for improving individual predictions of node attributes. Using
this framework, we quantify the assortativity range leading to an
accuracy gain in several situations. We finally show how specific
characteristics of the network can improve performances further.
For instance, the gender assortativity in real-world mobile phone
data changes significantly according to some communication
attributes. In this case, individual predictions with 75% accuracy
are improved by up to 3%.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://wsom2017.loria.fr/
