Graphlet decomposition of a weighted network

Hossein Azari Soufiani, Edo Airoldi
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:54-63, 2012.

Abstract

We consider the problem of modeling networks with nonnegative edge weights. We develop a \emphbit-string decomposition (BSD) for weighted networks, a new representation of social information based on social structure, with an underlying semi-parametric statistical model. We develop a scalable inference algorithm, which combines Expectation-Maximization with Bron-Kerbosch in a novel fashion, for estimating the model’s parameters from a network sample. We present theoretical descriptions to the computational complexity of the method. Finally, we demonstrate the performance of the proposed methodology for synthetic data, academic networks from Facebook and finding communities in a historical data from 19th century.

Cite this Paper

BibTeX
@InProceedings{pmlr-v22-azari12, title = {Graphlet decomposition of a weighted network}, author = {Soufiani, Hossein Azari and Airoldi, Edo}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {54--63}, year = {2012}, editor = {Lawrence, Neil D. and Girolami, Mark}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/azari12/azari12.pdf}, url = {https://proceedings.mlr.press/v22/azari12.html}, abstract = {We consider the problem of modeling networks with nonnegative edge weights. We develop a \emphbit-string decomposition (BSD) for weighted networks, a new representation of social information based on social structure, with an underlying semi-parametric statistical model. We develop a scalable inference algorithm, which combines Expectation-Maximization with Bron-Kerbosch in a novel fashion, for estimating the model’s parameters from a network sample. We present theoretical descriptions to the computational complexity of the method. Finally, we demonstrate the performance of the proposed methodology for synthetic data, academic networks from Facebook and finding communities in a historical data from 19th century.} }
Endnote
%0 Conference Paper %T Graphlet decomposition of a weighted network %A Hossein Azari Soufiani %A Edo Airoldi %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-azari12 %I PMLR %P 54--63 %U https://proceedings.mlr.press/v22/azari12.html %V 22 %X We consider the problem of modeling networks with nonnegative edge weights. We develop a \emphbit-string decomposition (BSD) for weighted networks, a new representation of social information based on social structure, with an underlying semi-parametric statistical model. We develop a scalable inference algorithm, which combines Expectation-Maximization with Bron-Kerbosch in a novel fashion, for estimating the model’s parameters from a network sample. We present theoretical descriptions to the computational complexity of the method. Finally, we demonstrate the performance of the proposed methodology for synthetic data, academic networks from Facebook and finding communities in a historical data from 19th century.
RIS
TY - CPAPER TI - Graphlet decomposition of a weighted network AU - Hossein Azari Soufiani AU - Edo Airoldi BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-azari12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 22 SP - 54 EP - 63 L1 - http://proceedings.mlr.press/v22/azari12/azari12.pdf UR - https://proceedings.mlr.press/v22/azari12.html AB - We consider the problem of modeling networks with nonnegative edge weights. We develop a \emphbit-string decomposition (BSD) for weighted networks, a new representation of social information based on social structure, with an underlying semi-parametric statistical model. We develop a scalable inference algorithm, which combines Expectation-Maximization with Bron-Kerbosch in a novel fashion, for estimating the model’s parameters from a network sample. We present theoretical descriptions to the computational complexity of the method. Finally, we demonstrate the performance of the proposed methodology for synthetic data, academic networks from Facebook and finding communities in a historical data from 19th century. ER -
APA
Soufiani, H.A. & Airoldi, E.. (2012). Graphlet decomposition of a weighted network. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 22:54-63 Available from https://proceedings.mlr.press/v22/azari12.html.

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