On the Statistical Estimation of the Preferential Attachment Network Model

42 mins 30 secs,  77.75 MB,  MP3  44100 Hz,  249.78 kbits/sec
Share this media item:
Embed this media item:


About this item
Image inherited from collection
Description: Gao, F (Universiteit Leiden)
Friday 16th December 2016 - 13:30 to 14:15
 
Created: 2016-12-21 10:47
Collection: Theoretical Foundations for Statistical Network Analysis
Publisher: Isaac Newton Institute
Copyright: Gao, F
Language: eng (English)
Distribution: World     (downloadable)
Explicit content: No
Aspect Ratio: 16:9
Screencast: No
Bumper: UCS Default
Trailer: UCS Default
 
Abstract: The preferential attachment (PA) network is a popular way of modeling the social networks, the collaboration networks and etc. The PA network model is an evolving network model where new nodes keep coming in. When a new node comes in, it establishes only one connection with an existing node. The random choice on the existing node is via a multinomial distribution with probability weights based on a preferential function f on the degrees. f maps the natural numbers to the positive real line and is assumed apriori non-decreasing, which means the nodes with high degrees are more likely to get new connections, i.e. "the rich get richer". We proposed an estimator on f. We show, with techniques from branching process, our estimator is consistent. If f is affine, meaning f(k) = k + delta, it is well known that such a model leads to a power-law degree distribution. We proposed a maximum likelihood estimator for delta and establish a central limit result on the MLE of delta. If f belongs to a parametric family no faster than linear, we show the MLE will also yield optimal performance with the asymptotic normality results. We will also talk about the potential extensions of the model (with borrowed strength from nonparametric Bayesian statistics) and interesting applications.
This is joint work with Aad van der Vaart.
Available Formats
Format Quality Bitrate Size
MPEG-4 Video 640x360    1.94 Mbits/sec 618.28 MB View Download
WebM 640x360    616.35 kbits/sec 191.71 MB View Download
iPod Video 480x270    522.14 kbits/sec 162.41 MB View Download
MP3 * 44100 Hz 249.78 kbits/sec 77.75 MB Listen Download
Auto (Allows browser to choose a format it supports)