17,093 research outputs found
A network-based dynamical ranking system for competitive sports
From the viewpoint of networks, a ranking system for players or teams in
sports is equivalent to a centrality measure for sports networks, whereby a
directed link represents the result of a single game. Previously proposed
network-based ranking systems are derived from static networks, i.e.,
aggregation of the results of games over time. However, the score of a player
(or team) fluctuates over time. Defeating a renowned player in the peak
performance is intuitively more rewarding than defeating the same player in
other periods. To account for this factor, we propose a dynamic variant of such
a network-based ranking system and apply it to professional men's tennis data.
We derive a set of linear online update equations for the score of each player.
The proposed ranking system predicts the outcome of the future games with a
higher accuracy than the static counterparts.Comment: 6 figure
Reconstructing dynamical networks via feature ranking
Empirical data on real complex systems are becoming increasingly available.
Parallel to this is the need for new methods of reconstructing (inferring) the
topology of networks from time-resolved observations of their node-dynamics.
The methods based on physical insights often rely on strong assumptions about
the properties and dynamics of the scrutinized network. Here, we use the
insights from machine learning to design a new method of network reconstruction
that essentially makes no such assumptions. Specifically, we interpret the
available trajectories (data) as features, and use two independent feature
ranking approaches -- Random forest and RReliefF -- to rank the importance of
each node for predicting the value of each other node, which yields the
reconstructed adjacency matrix. We show that our method is fairly robust to
coupling strength, system size, trajectory length and noise. We also find that
the reconstruction quality strongly depends on the dynamical regime
Diffusion of scientific credits and the ranking of scientists
Recently, the abundance of digital data enabled the implementation of graph
based ranking algorithms that provide system level analysis for ranking
publications and authors. Here we take advantage of the entire Physical Review
publication archive (1893-2006) to construct authors' networks where weighted
edges, as measured from opportunely normalized citation counts, define a proxy
for the mechanism of scientific credit transfer. On this network we define a
ranking method based on a diffusion algorithm that mimics the spreading of
scientific credits on the network. We compare the results obtained with our
algorithm with those obtained by local measures such as the citation count and
provide a statistical analysis of the assignment of major career awards in the
area of Physics. A web site where the algorithm is made available to perform
customized rank analysis can be found at the address
http://www.physauthorsrank.orgComment: Revised version. 11 pages, 10 figures, 1 table. The portal to compute
the rankings of scientists is at http://www.physauthorsrank.or
A measure of individual role in collective dynamics
Identifying key players in collective dynamics remains a challenge in several
research fields, from the efficient dissemination of ideas to drug target
discovery in biomedical problems. The difficulty lies at several levels: how to
single out the role of individual elements in such intermingled systems, or
which is the best way to quantify their importance. Centrality measures
describe a node's importance by its position in a network. The key issue
obviated is that the contribution of a node to the collective behavior is not
uniquely determined by the structure of the system but it is a result of the
interplay between dynamics and network structure. We show that dynamical
influence measures explicitly how strongly a node's dynamical state affects
collective behavior. For critical spreading, dynamical influence targets nodes
according to their spreading capabilities. For diffusive processes it
quantifies how efficiently real systems may be controlled by manipulating a
single node.Comment: accepted for publication in Scientific Report
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