984 research outputs found
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
Finding community structure in very large networks
The discovery and analysis of community structure in networks is a topic of
considerable recent interest within the physics community, but most methods
proposed so far are unsuitable for very large networks because of their
computational cost. Here we present a hierarchical agglomeration algorithm for
detecting community structure which is faster than many competing algorithms:
its running time on a network with n vertices and m edges is O(m d log n) where
d is the depth of the dendrogram describing the community structure. Many
real-world networks are sparse and hierarchical, with m ~ n and d ~ log n, in
which case our algorithm runs in essentially linear time, O(n log^2 n). As an
example of the application of this algorithm we use it to analyze a network of
items for sale on the web-site of a large online retailer, items in the network
being linked if they are frequently purchased by the same buyer. The network
has more than 400,000 vertices and 2 million edges. We show that our algorithm
can extract meaningful communities from this network, revealing large-scale
patterns present in the purchasing habits of customers
Vertex similarity in networks
We consider methods for quantifying the similarity of vertices in networks.
We propose a measure of similarity based on the concept that two vertices are
similar if their immediate neighbors in the network are themselves similar.
This leads to a self-consistent matrix formulation of similarity that can be
evaluated iteratively using only a knowledge of the adjacency matrix of the
network. We test our similarity measure on computer-generated networks for
which the expected results are known, and on a number of real-world networks
Signatures of currency vertices
Many real-world networks have broad degree distributions. For some systems,
this means that the functional significance of the vertices is also broadly
distributed, in other cases the vertices are equally significant, but in
different ways. One example of the latter case is metabolic networks, where the
high-degree vertices -- the currency metabolites -- supply the molecular groups
to the low-degree metabolites, and the latter are responsible for the
higher-order biological function, of vital importance to the organism. In this
paper, we propose a generalization of currency metabolites to currency
vertices. We investigate the network structural characteristics of such
systems, both in model networks and in some empirical systems. In addition to
metabolic networks, we find that a network of music collaborations and a
network of e-mail exchange could be described by a division of the vertices
into currency vertices and others.Comment: to appear in Journal of the Physical Society of Japa
Emergence of Symmetry in Complex Networks
Many real networks have been found to have a rich degree of symmetry, which
is a very important structural property of complex network, yet has been rarely
studied so far. And where does symmetry comes from has not been explained. To
explore the mechanism underlying symmetry of the networks, we studied
statistics of certain local symmetric motifs, such as symmetric bicliques and
generalized symmetric bicliques, which contribute to local symmetry of
networks. We found that symmetry of complex networks is a consequence of
similar linkage pattern, which means that nodes with similar degree tend to
share similar linkage targets. A improved version of BA model integrating
similar linkage pattern successfully reproduces the symmetry of real networks,
indicating that similar linkage pattern is the underlying ingredient that
responsible for the emergence of the symmetry in complex networks.Comment: 7 pages, 7 figure
Multistage Random Growing Small-World Networks with Power-law degree Distribution
In this paper, a simply rule that generates scale-free networks with very
large clustering coefficient and very small average distance is presented.
These networks are called {\bf Multistage Random Growing Networks}(MRGN) as the
adding process of a new node to the network is composed of two stages. The
analytic results of power-law exponent and clustering coefficient
are obtained, which agree with the simulation results approximately.
In addition, the average distance of the networks increases logarithmical with
the number of the network vertices is proved analytically. Since many real-life
networks are both scale-free and small-world networks, MRGN may perform well in
mimicking reality.Comment: 3 figures, 4 page
Robust modeling of human contact networks across different scales and proximity-sensing techniques
The problem of mapping human close-range proximity networks has been tackled
using a variety of technical approaches. Wearable electronic devices, in
particular, have proven to be particularly successful in a variety of settings
relevant for research in social science, complex networks and infectious
diseases dynamics. Each device and technology used for proximity sensing (e.g.,
RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with
specific biases on the close-range relations it records. Hence it is important
to assess which statistical features of the empirical proximity networks are
robust across different measurement techniques, and which modeling frameworks
generalize well across empirical data. Here we compare time-resolved proximity
networks recorded in different experimental settings and show that some
important statistical features are robust across all settings considered. The
observed universality calls for a simplified modeling approach. We show that
one such simple model is indeed able to reproduce the main statistical
distributions characterizing the empirical temporal networks
Attack Resilience of the Evolving Scientific Collaboration Network
Stationary complex networks have been extensively studied in the last ten years. However, many natural systems are known to be continuously evolving at the local (âmicroscopicâ) level. Understanding the response to targeted attacks of an evolving network may shed light on both how to design robust systems and finding effective attack strategies. In this paper we study empirically the response to targeted attacks of the scientific collaboration networks. First we show that scientific collaboration network is a complex system which evolves intensively at the local level â fewer than 20% of scientific collaborations last more than one year. Then, we investigate the impact of the sudden death of eminent scientists on the evolution of the collaboration networks of their former collaborators. We observe in particular that the sudden death, which is equivalent to the removal of the center of the egocentric network of the eminent scientist, does not affect the topological evolution of the residual network. Nonetheless, removal of the eminent hub node is exactly the strategy one would adopt for an effective targeted attack on a stationary network. Hence, we use this evolving collaboration network as an experimental model for attack on an evolving complex network. We find that such attacks are ineffectual, and infer that the scientific collaboration network is the trace of knowledge propagation on a larger underlying social network. The redundancy of the underlying structure in fact acts as a protection mechanism against such network attacks
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The distribution of the ring current: Cluster observations
Extending previous studies, a full-circle investigation of the ring current has been made using Cluster 4-spacecraft observations near perigee, at times when the Cluster array had relatively small separations and nearly regular tetrahedral configurations, and when the Dst index was greater than â30 nT (non-storm conditions). These observations result in direct estimations of the near equatorial current density at all magnetic local times (MLT) for the first time and with sufficient accuracy, for the following observations. The results confirm that the ring current flows westward and show that the in situ average measured current density (sampled in the radial range accessed by Cluster 4â4.5RE) is asymmetric in MLT, ranging from 9 to 27 nAmâ2. The direction of current is shown to be very well ordered for the whole range of MLT. Both of these results are in line with previous studies on partial ring extent. The magnitude of the current density, however, reveals a distinct asymmetry: growing from 10 to 27 nAmâ2 as azimuth reduces from about 12:00MLT to 03:00 and falling from 20 to 10 nAmâ2 less steadily as azimuth reduces from 24:00 to 12:00MLT. This result has not been reported before and we suggest it could reflect a number of effects. Firstly, we argue it is consistent with the operation of region-2 field aligned-currents (FACs), which are expected to flow upward into the ring current around 09:00MLT and downward out of the ring current around 14:00MLT. Secondly, we note that it is also consistent with a possible asymmetry in the radial distribution profile of current density (resulting in higher peak at 4â 4.5RE). We note that part of the enhanced current could reflect an increase in the mean AE activity (during the periods in which Cluster samples those MLT)
Theoretical approach and impact of correlations on the critical packet generation rate in traffic dynamics on complex networks
Using the formalism of the biased random walk in random uncorrelated networks
with arbitrary degree distributions, we develop theoretical approach to the
critical packet generation rate in traffic based on routing strategy with local
information. We explain microscopic origins of the transition from the flow to
the jammed phase and discuss how the node neighbourhood topology affects the
transport capacity in uncorrelated and correlated networks.Comment: 6 pages, 5 figure
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