3,224 research outputs found
Multiple dynamical time-scales in networks with hierarchically nested modular organization
Many natural and engineered complex networks have intricate mesoscopic
organization, e.g., the clustering of the constituent nodes into several
communities or modules. Often, such modularity is manifested at several
different hierarchical levels, where the clusters defined at one level appear
as elementary entities at the next higher level. Using a simple model of a
hierarchical modular network, we show that such a topological structure gives
rise to characteristic time-scale separation between dynamics occurring at
different levels of the hierarchy. This generalizes our earlier result for
simple modular networks, where fast intra-modular and slow inter-modular
processes were clearly distinguished. Investigating the process of
synchronization of oscillators in a hierarchical modular network, we show the
existence of as many distinct time-scales as there are hierarchical levels in
the system. This suggests a possible functional role of such mesoscopic
organization principle in natural systems, viz., in the dynamical separation of
events occurring at different spatial scales.Comment: 10 pages, 4 figure
The evolution of interdisciplinarity in physics research
Science, being a social enterprise, is subject to fragmentation into groups
that focus on specialized areas or topics. Often new advances occur through
cross-fertilization of ideas between sub-fields that otherwise have little
overlap as they study dissimilar phenomena using different techniques. Thus to
explore the nature and dynamics of scientific progress one needs to consider
the large-scale organization and interactions between different subject areas.
Here, we study the relationships between the sub-fields of Physics using the
Physics and Astronomy Classification Scheme (PACS) codes employed for
self-categorization of articles published over the past 25 years (1985-2009).
We observe a clear trend towards increasing interactions between the different
sub-fields. The network of sub-fields also exhibits core-periphery
organization, the nucleus being dominated by Condensed Matter and General
Physics. However, over time Interdisciplinary Physics is steadily increasing
its share in the network core, reflecting a shift in the overall trend of
Physics research.Comment: Published version, 10 pages, 8 figures + Supplementary Informatio
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
A Universal Lifetime Distribution for Multi-Species Systems
Lifetime distributions of social entities, such as enterprises, products, and
media contents, are one of the fundamental statistics characterizing the social
dynamics. To investigate the lifetime distribution of mutually interacting
systems, simple models having a rule for additions and deletions of entities
are investigated. We found a quite universal lifetime distribution for various
kinds of inter-entity interactions, and it is well fitted by a
stretched-exponential function with an exponent close to 1/2. We propose a
"modified Red-Queen" hypothesis to explain this distribution. We also review
empirical studies on the lifetime distribution of social entities, and
discussed the applicability of the model.Comment: 10 pages, 6 figures, Proceedings of Social Modeling and Simulations +
Econophysics Colloquium 201
Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data
Use of socially generated "big data" to access information about collective
states of the minds in human societies has become a new paradigm in the
emerging field of computational social science. A natural application of this
would be the prediction of the society's reaction to a new product in the sense
of popularity and adoption rate. However, bridging the gap between "real time
monitoring" and "early predicting" remains a big challenge. Here we report on
an endeavor to build a minimalistic predictive model for the financial success
of movies based on collective activity data of online users. We show that the
popularity of a movie can be predicted much before its release by measuring and
analyzing the activity level of editors and viewers of the corresponding entry
to the movie in Wikipedia, the well-known online encyclopedia.Comment: 13 pages, Including Supporting Information, 7 Figures, Download the
dataset from: http://wwm.phy.bme.hu/SupplementaryDataS1.zi
Mesoscopic organization reveals the constraints governing C. elegans nervous system
One of the biggest challenges in biology is to understand how activity at the
cellular level of neurons, as a result of their mutual interactions, leads to
the observed behavior of an organism responding to a variety of environmental
stimuli. Investigating the intermediate or mesoscopic level of organization in
the nervous system is a vital step towards understanding how the integration of
micro-level dynamics results in macro-level functioning. In this paper, we have
considered the somatic nervous system of the nematode Caenorhabditis elegans,
for which the entire neuronal connectivity diagram is known. We focus on the
organization of the system into modules, i.e., neuronal groups having
relatively higher connection density compared to that of the overall network.
We show that this mesoscopic feature cannot be explained exclusively in terms
of considerations, such as optimizing for resource constraints (viz., total
wiring cost) and communication efficiency (i.e., network path length).
Comparison with other complex networks designed for efficient transport (of
signals or resources) implies that neuronal networks form a distinct class.
This suggests that the principal function of the network, viz., processing of
sensory information resulting in appropriate motor response, may be playing a
vital role in determining the connection topology. Using modular spectral
analysis, we make explicit the intimate relation between function and structure
in the nervous system. This is further brought out by identifying functionally
critical neurons purely on the basis of patterns of intra- and inter-modular
connections. Our study reveals how the design of the nervous system reflects
several constraints, including its key functional role as a processor of
information.Comment: Published version, Minor modifications, 16 pages, 9 figure
World citation and collaboration networks: uncovering the role of geography in science
Modern information and communication technologies, especially the Internet,
have diminished the role of spatial distances and territorial boundaries on the
access and transmissibility of information. This has enabled scientists for
closer collaboration and internationalization. Nevertheless, geography remains
an important factor affecting the dynamics of science. Here we present a
systematic analysis of citation and collaboration networks between cities and
countries, by assigning papers to the geographic locations of their authors'
affiliations. The citation flows as well as the collaboration strengths between
cities decrease with the distance between them and follow gravity laws. In
addition, the total research impact of a country grows linearly with the amount
of national funding for research & development. However, the average impact
reveals a peculiar threshold effect: the scientific output of a country may
reach an impact larger than the world average only if the country invests more
than about 100,000 USD per researcher annually.Comment: Published version. 9 pages, 5 figures + Appendix, The world citation
and collaboration networks at both city and country level are available at
http://becs.aalto.fi/~rajkp/datasets.htm
Graph Metrics for Temporal Networks
Temporal networks, i.e., networks in which the interactions among a set of
elementary units change over time, can be modelled in terms of time-varying
graphs, which are time-ordered sequences of graphs over a set of nodes. In such
graphs, the concepts of node adjacency and reachability crucially depend on the
exact temporal ordering of the links. Consequently, all the concepts and
metrics proposed and used for the characterisation of static complex networks
have to be redefined or appropriately extended to time-varying graphs, in order
to take into account the effects of time ordering on causality. In this chapter
we discuss how to represent temporal networks and we review the definitions of
walks, paths, connectedness and connected components valid for graphs in which
the links fluctuate over time. We then focus on temporal node-node distance,
and we discuss how to characterise link persistence and the temporal
small-world behaviour in this class of networks. Finally, we discuss the
extension of classic centrality measures, including closeness, betweenness and
spectral centrality, to the case of time-varying graphs, and we review the work
on temporal motifs analysis and the definition of modularity for temporal
graphs.Comment: 26 pages, 5 figures, Chapter in Temporal Networks (Petter Holme and
Jari Saram\"aki editors). Springer. Berlin, Heidelberg 201
Random Walks on Stochastic Temporal Networks
In the study of dynamical processes on networks, there has been intense focus
on network structure -- i.e., the arrangement of edges and their associated
weights -- but the effects of the temporal patterns of edges remains poorly
understood. In this chapter, we develop a mathematical framework for random
walks on temporal networks using an approach that provides a compromise between
abstract but unrealistic models and data-driven but non-mathematical
approaches. To do this, we introduce a stochastic model for temporal networks
in which we summarize the temporal and structural organization of a system
using a matrix of waiting-time distributions. We show that random walks on
stochastic temporal networks can be described exactly by an
integro-differential master equation and derive an analytical expression for
its asymptotic steady state. We also discuss how our work might be useful to
help build centrality measures for temporal networks.Comment: Chapter in Temporal Networks (Petter Holme and Jari Saramaki
editors). Springer. Berlin, Heidelberg 2013. The book chapter contains minor
corrections and modifications. This chapter is based on arXiv:1112.3324,
which contains additional calculations and numerical simulation
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