Graphlet decomposition of a weighted network

Details Graphlet decomposition of a weighted network Graphlet decomposition of a weighted network

2012-03-13

arxiv.org/abs/1203.2821v1

Journal of Machine Learning Research, Workshop & Conference Proceedings, vol. 22 (AISTATS), 2012

Statistics

Methodology

25 pages, 4 figures, 3 tables

Scientific paper

We introduce the graphlet decomposition of a weighted network, which encodes a notion of social information based on social structure. We develop a scalable inference algorithm, which combines EM with Bron-Kerbosch in a novel fashion, for estimating the parameters of the model underlying graphlets using one network sample. We explore some theoretical properties of the graphlet decomposition, including computational complexity, redundancy and expected accuracy. We demonstrate graphlets on synthetic and real data. We analyze messaging patterns on Facebook and criminal associations in the 19th century.

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