61,213 research outputs found
Detecting change points in the large-scale structure of evolving networks
Interactions among people or objects are often dynamic in nature and can be
represented as a sequence of networks, each providing a snapshot of the
interactions over a brief period of time. An important task in analyzing such
evolving networks is change-point detection, in which we both identify the
times at which the large-scale pattern of interactions changes fundamentally
and quantify how large and what kind of change occurred. Here, we formalize for
the first time the network change-point detection problem within an online
probabilistic learning framework and introduce a method that can reliably solve
it. This method combines a generalized hierarchical random graph model with a
Bayesian hypothesis test to quantitatively determine if, when, and precisely
how a change point has occurred. We analyze the detectability of our method
using synthetic data with known change points of different types and
magnitudes, and show that this method is more accurate than several previously
used alternatives. Applied to two high-resolution evolving social networks,
this method identifies a sequence of change points that align with known
external "shocks" to these networks
Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network
The network of patents connected by citations is an evolving graph, which
provides a representation of the innovation process. A patent citing another
implies that the cited patent reflects a piece of previously existing knowledge
that the citing patent builds upon. A methodology presented here (i) identifies
actual clusters of patents: i.e. technological branches, and (ii) gives
predictions about the temporal changes of the structure of the clusters. A
predictor, called the {citation vector}, is defined for characterizing
technological development to show how a patent cited by other patents belongs
to various industrial fields. The clustering technique adopted is able to
detect the new emerging recombinations, and predicts emerging new technology
clusters. The predictive ability of our new method is illustrated on the
example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of
patents is determined based on citation data up to 1991, which shows
significant overlap of the class 442 formed at the beginning of 1997. These new
tools of predictive analytics could support policy decision making processes in
science and technology, and help formulate recommendations for action
Detection and localization of change points in temporal networks with the aid of stochastic block models
A framework based on generalized hierarchical random graphs (GHRGs) for the
detection of change points in the structure of temporal networks has recently
been developed by Peel and Clauset [1]. We build on this methodology and extend
it to also include the versatile stochastic block models (SBMs) as a parametric
family for reconstructing the empirical networks. We use five different
techniques for change point detection on prototypical temporal networks,
including empirical and synthetic ones. We find that none of the considered
methods can consistently outperform the others when it comes to detecting and
locating the expected change points in empirical temporal networks. With
respect to the precision and the recall of the results of the change points, we
find that the method based on a degree-corrected SBM has better recall
properties than other dedicated methods, especially for sparse networks and
smaller sliding time window widths.Comment: This is an author-created, un-copyedited version of an article
accepted for publication/published in Journal of Statistical Mechanics:
Theory and Experiment. IOP Publishing Ltd is not responsible for any errors
or omissions in this version of the manuscript or any version derived from
it. The Version of Record is available online at
http://dx.doi.org/10.1088/1742-5468/2016/11/11330
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