30,060 research outputs found
Fragmentation of Random Trees
We study fragmentation of a random recursive tree into a forest by repeated
removal of nodes. The initial tree consists of N nodes and it is generated by
sequential addition of nodes with each new node attaching to a
randomly-selected existing node. As nodes are removed from the tree, one at a
time, the tree dissolves into an ensemble of separate trees, namely, a forest.
We study statistical properties of trees and nodes in this heterogeneous
forest, and find that the fraction of remaining nodes m characterizes the
system in the limit N --> infty. We obtain analytically the size density phi_s
of trees of size s. The size density has power-law tail phi_s ~ s^(-alpha) with
exponent alpha=1+1/m. Therefore, the tail becomes steeper as further nodes are
removed, and the fragmentation process is unusual in that exponent alpha
increases continuously with time. We also extend our analysis to the case where
nodes are added as well as removed, and obtain the asymptotic size density for
growing trees.Comment: 9 pages, 5 figure
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics
The combination of multiple classifiers using ensemble methods is
increasingly important for making progress in a variety of difficult prediction
problems. We present a comparative analysis of several ensemble methods through
two case studies in genomics, namely the prediction of genetic interactions and
protein functions, to demonstrate their efficacy on real-world datasets and
draw useful conclusions about their behavior. These methods include simple
aggregation, meta-learning, cluster-based meta-learning, and ensemble selection
using heterogeneous classifiers trained on resampled data to improve the
diversity of their predictions. We present a detailed analysis of these methods
across 4 genomics datasets and find the best of these methods offer
statistically significant improvements over the state of the art in their
respective domains. In addition, we establish a novel connection between
ensemble selection and meta-learning, demonstrating how both of these disparate
methods establish a balance between ensemble diversity and performance.Comment: 10 pages, 3 figures, 8 tables, to appear in Proceedings of the 2013
International Conference on Data Minin
Hyperbolic Geometry of Complex Networks
We develop a geometric framework to study the structure and function of
complex networks. We assume that hyperbolic geometry underlies these networks,
and we show that with this assumption, heterogeneous degree distributions and
strong clustering in complex networks emerge naturally as simple reflections of
the negative curvature and metric property of the underlying hyperbolic
geometry. Conversely, we show that if a network has some metric structure, and
if the network degree distribution is heterogeneous, then the network has an
effective hyperbolic geometry underneath. We then establish a mapping between
our geometric framework and statistical mechanics of complex networks. This
mapping interprets edges in a network as non-interacting fermions whose
energies are hyperbolic distances between nodes, while the auxiliary fields
coupled to edges are linear functions of these energies or distances. The
geometric network ensemble subsumes the standard configuration model and
classical random graphs as two limiting cases with degenerate geometric
structures. Finally, we show that targeted transport processes without global
topology knowledge, made possible by our geometric framework, are maximally
efficient, according to all efficiency measures, in networks with strongest
heterogeneity and clustering, and that this efficiency is remarkably robust
with respect to even catastrophic disturbances and damages to the network
structure
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