778 research outputs found
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
Emergence of communities in weighted networks
Topology and weights are closely related in weighted complex networks and
this is reflected in their modular structure. We present a simple network model
where the weights are generated dynamically and they shape the developing
topology. By tuning a model parameter governing the importance of weights, the
resulting networks undergo a gradual structural transition from a module free
topology to one with communities. The model also reproduces many features of
large social networks, including the "weak links" property.Comment: 4 pages, 5 figure
Master-equation analysis of accelerating networks
In many real-world networks, the rates of node and link addition are time
dependent. This observation motivates the definition of accelerating networks.
There has been relatively little investigation of accelerating networks and
previous efforts at analyzing their degree distributions have employed
mean-field techniques. By contrast, we show that it is possible to apply a
master-equation approach to such network development. We provide full
time-dependent expressions for the evolution of the degree distributions for
the canonical situations of random and preferential attachment in networks
undergoing constant acceleration. These results are in excellent agreement with
results obtained from simulations. We note that a growing, non-equilibrium
network undergoing constant acceleration with random attachment is equivalent
to a classical random graph, bridging the gap between non-equilibrium and
classical equilibrium networks.Comment: 6 pages, 1 figure, 1 tabl
Correlation based networks of equity returns sampled at different time horizons
We investigate the planar maximally filtered graphs of the portfolio of the
300 most capitalized stocks traded at the New York Stock Exchange during the
time period 2001-2003. Topological properties such as the average length of
shortest paths, the betweenness and the degree are computed on different planar
maximally filtered graphs generated by sampling the returns at different time
horizons ranging from 5 min up to one trading day. This analysis confirms that
the selected stocks compose a hierarchical system progressively structuring as
the sampling time horizon increases. Finally, a cluster formation, associated
to economic sectors, is quantitatively investigated.Comment: 9 pages, 8 figure
Effects of time window size and placement on the structure of aggregated networks
Complex networks are often constructed by aggregating empirical data over
time, such that a link represents the existence of interactions between the
endpoint nodes and the link weight represents the intensity of such
interactions within the aggregation time window. The resulting networks are
then often considered static. More often than not, the aggregation time window
is dictated by the availability of data, and the effects of its length on the
resulting networks are rarely considered. Here, we address this question by
studying the structural features of networks emerging from aggregating
empirical data over different time intervals, focussing on networks derived
from time-stamped, anonymized mobile telephone call records. Our results show
that short aggregation intervals yield networks where strong links associated
with dense clusters dominate; the seeds of such clusters or communities become
already visible for intervals of around one week. The degree and weight
distributions are seen to become stationary around a few days and a few weeks,
respectively. An aggregation interval of around 30 days results in the stablest
similar networks when consecutive windows are compared. For longer intervals,
the effects of weak or random links become increasingly stronger, and the
average degree of the network keeps growing even for intervals up to 180 days.
The placement of the time window is also seen to affect the outcome: for short
windows, different behavioural patterns play a role during weekends and
weekdays, and for longer windows it is seen that networks aggregated during
holiday periods are significantly different.Comment: 19 pages, 11 figure
Sampling bias in systems with structural heterogeneity and limited internal diffusion
Complex systems research is becomingly increasingly data-driven, particularly
in the social and biological domains. Many of the systems from which sample
data are collected feature structural heterogeneity at the mesoscopic scale
(i.e. communities) and limited inter-community diffusion. Here we show that the
interplay between these two features can yield a significant bias in the global
characteristics inferred from the data. We present a general framework to
quantify this bias, and derive an explicit corrective factor for a wide class
of systems. Applying our analysis to a recent high-profile survey of conflict
mortality in Iraq suggests a significant overestimate of deaths
Close relationships: A study of mobile communication records
Mobile phone communication as digital service generates ever-increasing
datasets of human communication actions, which in turn allow us to investigate
the structure and evolution of social interactions and their networks. These
datasets can be used to study the structuring of such egocentric networks with
respect to the strength of the relationships by assuming direct dependence of
the communication intensity on the strength of the social tie. Recently we have
discovered that there are significant differences between the first and further
"best friends" from the point of view of age and gender preferences. Here we
introduce a control parameter based on the statistics of
communication with the first and second "best friend" and use it to filter the
data. We find that when is decreased the identification of the
"best friend" becomes less ambiguous and the earlier observed effects get
stronger, thus corroborating them.Comment: 11 pages, 7 figure
Networks of companies and branches in Poland
In this study we consider relations between companies in Poland taking into
account common branches they belong to. It is clear that companies belonging to
the same branch compete for similar customers, so the market induces
correlations between them. On the other hand two branches can be related by
companies acting in both of them. To remove weak, accidental links we shall use
a concept of threshold filtering for weighted networks where a link weight
corresponds to a number of existing connections (common companies or branches)
between a pair of nodes.Comment: 13 pages, 10 figures and 4 table
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