Sparse and modular networks using exchangeable random measures

Add to your list(s) Download to your calendar using vCal

• Francois Caron (University of Oxford)
• Monday 11 July 2016, 15:30-16:00
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

SNAW01 - Graph limits and statistics

Statistical network modeling has focused on representing the graph as a discrete structure, namely the adjacency matrix, and considering the exchangeability of this array. In such cases, it is well known that the graph is necessarily either dense (the number of edges scales quadratically with the number of nodes) or trivially empty.
Here, we instead consider representing the graph as a measure on the plane. For the associated definition of exchangeability, we rely on the Kallenberg representation theorem (Kallenberg, 1990). For certain choices of such exchangeable random measures underlying the graph construction, the network process is sparse with power-law degree distribution, and can accommodate an overlapping block-structure.
A Markov chain Monte Carlo algorithm is derived for efficient exploration of the posterior distribution and allows to recover the structure of a range of networks ranging from dense to sparse based on our flexible formulation.

Joint work with Emily Fox and Adrien Todeschini
http://arxiv.org/abs/1401.1137
http://arxiv.org/pdf/1602.02114

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge talks
• Chris Davis' list
• Featured lists
• INI info aggregator
• Interested Talks
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 1, Newton Institute
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587

Note that ex-directory lists are not shown.