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(Seminar)Inferring the large-scale structure of weighted networks
2018-01-19     Text Size:  A

CAS Key Laboratory of Theoretical Physics

Institute of Theoretical Physics

 Chinese Academy of Sciences

Seminar

Title

题目

Inferring the large-scale structure of weighted networks

Speaker

报告人

Tiago P. Peixoto

Affiliation

所在单位

University of Bath, UK

Date

日期

2018年1月19日(周五)上午10:30

Venue

地点

ITP New Building 6420

Abstract

摘要

Networks form the backbone of a wide variety of complex systems, ranging from food webs, gene regulation, social networks, transportation and the internet. However, due to the sheer size and complexity of many of theses systems, it remains an open challenge to formulate general descriptions of their structures, and to extract such information from data. Since networks are high-dimensional relational objects, they cannot be directly inspected using basic tools, and instead require new methodology. In this talk, I present a Bayesian formulation of weighted stochastic block models that can be used to infer the large-scale modular structure of weighted networks, including their hierarchical organization. Our method is nonparametric, and thus does not require the prior knowledge of the number of modules or other dimensions of the model, which are instead inferred from data. We give a comprehensive treatment of different kinds of edge weights (i.e. continuous or discrete, signed or unsigned, bounded or unbounded), as well as arbitrary weight transformations, and describe an unsupervised model selection approach to choose the best network description. We illustrate the application of our method to a variety of empirical weighted networks, such as global migrations, voting patterns in congress, and neural connections in the human brain.

Contact person

所内联系人

张潘

 

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