4 research outputs found
Efficient inference of overlapping communities in complex networks
We discuss two views on extending existing methods for complex network
modeling which we dub the communities first and the networks first view,
respectively. Inspired by the networks first view that we attribute to White,
Boorman, and Breiger (1976)[1], we formulate the multiple-networks stochastic
blockmodel (MNSBM), which seeks to separate the observed network into
subnetworks of different types and where the problem of inferring structure in
each subnetwork becomes easier. We show how this model is specified in a
generative Bayesian framework where parameters can be inferred efficiently
using Gibbs sampling. The result is an effective multiple-membership model
without the drawbacks of introducing complex definitions of "groups" and how
they interact. We demonstrate results on the recovery of planted structure in
synthetic networks and show very encouraging results on link prediction
performances using multiple-networks models on a number of real-world network
data sets
Dimensionality reduction for click-through rate prediction: Dense versus sparse representation
In online advertising, display ads are increasingly being placed based on
real-time auctions where the advertiser who wins gets to serve the ad. This is
called real-time bidding (RTB). In RTB, auctions have very tight time
constraints on the order of 100ms. Therefore mechanisms for bidding
intelligently such as clickthrough rate prediction need to be sufficiently
fast. In this work, we propose to use dimensionality reduction of the
user-website interaction graph in order to produce simplified features of users
and websites that can be used as predictors of clickthrough rate. We
demonstrate that the Infinite Relational Model (IRM) as a dimensionality
reduction offers comparable predictive performance to conventional
dimensionality reduction schemes, while achieving the most economical usage of
features and fastest computations at run-time. For applications such as
real-time bidding, where fast database I/O and few computations are key to
success, we thus recommend using IRM based features as predictors to exploit
the recommender effects from bipartite graphs.Comment: Presented at the Probabilistic Models for Big Data workshop at NIPS
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