2,047 research outputs found
Role-similarity based functional prediction in networked systems: Application to the yeast proteome
We propose a general method to predict functions of vertices where: 1. The
wiring of the network is somehow related to the vertex functionality. 2. A
fraction of the vertices are functionally classified. The method is influenced
by role-similarity measures of social network analysis. The two versions of our
prediction scheme is tested on model networks were the functions of the
vertices are designed to match their network surroundings. We also apply these
methods to the proteome of the yeast Saccharomyces cerevisiae and find the
results compatible with more specialized methods
Core-periphery organization of complex networks
Networks may, or may not, be wired to have a core that is both itself densely
connected and central in terms of graph distance. In this study we propose a
coefficient to measure if the network has such a clear-cut core-periphery
dichotomy. We measure this coefficient for a number of real-world and model
networks and find that different classes of networks have their characteristic
values. For example do geographical networks have a strong core-periphery
structure, while the core-periphery structure of social networks (despite their
positive degree-degree correlations) is rather weak. We proceed to study radial
statistics of the core, i.e. properties of the n-neighborhoods of the core
vertices for increasing n. We find that almost all networks have unexpectedly
many edges within n-neighborhoods at a certain distance from the core
suggesting an effective radius for non-trivial network processes
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
The diplomat's dilemma: Maximal power for minimal effort in social networks
Closeness is a global measure of centrality in networks, and a proxy for how
influential actors are in social networks. In most network models, and many
empirical networks, closeness is strongly correlated with degree. However, in
social networks there is a cost of maintaining social ties. This leads to a
situation (that can occur in the professional social networks of executives,
lobbyists, diplomats and so on) where agents have the conflicting objectives of
aiming for centrality while simultaneously keeping the degree low. We
investigate this situation in an adaptive network-evolution model where agents
optimize their positions in the network following individual strategies, and
using only local information. The strategies are also optimized, based on the
success of the agent and its neighbors. We measure and describe the time
evolution of the network and the agents' strategies.Comment: Submitted to Adaptive Networks: Theory, Models and Applications, to
be published from Springe
Nonequilibrium phase transition in the coevolution of networks and opinions
Models of the convergence of opinion in social systems have been the subject
of a considerable amount of recent attention in the physics literature. These
models divide into two classes, those in which individuals form their beliefs
based on the opinions of their neighbors in a social network of personal
acquaintances, and those in which, conversely, network connections form between
individuals of similar beliefs. While both of these processes can give rise to
realistic levels of agreement between acquaintances, practical experience
suggests that opinion formation in the real world is not a result of one
process or the other, but a combination of the two. Here we present a simple
model of this combination, with a single parameter controlling the balance of
the two processes. We find that the model undergoes a continuous phase
transition as this parameter is varied, from a regime in which opinions are
arbitrarily diverse to one in which most individuals hold the same opinion. We
characterize the static and dynamical properties of this transition
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
The global migration network of sex-workers
Differences in the social and economic environment across countries encourage
humans to migrate in search of better living conditions, including job
opportunities, higher salaries, security and welfare. Quantifying global
migration is, however, challenging because of poor recording, privacy issues
and residence status. This is particularly critical for some classes of
migrants involved in stigmatised, unregulated or illegal activities. Escorting
services or high-end prostitution are well-paid activities that attract workers
all around the world. In this paper, we study international migration patterns
of sex-workers by using network methods. Using an extensive international
online advertisement directory of escorting services and information about
individual escorts, we reconstruct a migrant flow network where nodes represent
either origin or destination countries. The links represent the direct routes
between two countries. The migration network of sex-workers shows different
structural patterns than the migration of the general population. The network
contains a strong core where mutual migration is often observed between a group
of high-income European countries, yet Europe is split into different network
communities with specific ties to non-European countries. We find
non-reciprocal relations between countries, with some of them mostly offering
while others attract workers. The GDP per capita is a good indicator of country
attractiveness for incoming workers and service rates but is unrelated to the
probability of emigration. The median financial gain of migrating, in
comparison to working at the home country, is 15.9%. Only sex-workers coming
from 77% of the countries have financial gains with migration and average gains
decrease with the GDPc of the country of origin. Our results shows that
high-end sex-worker migration is regulated by economic, geographic and cultural
aspects.Comment: Comments and feedback welcomed. Two tables and 6 figures including S
Morphology of travel routes and the organization of cities
This is the final version. Available from Nature Research via the DOI in this recordData availability.
All data needed to evaluate the conclusions are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors and are also available at https://github.com/mlee96/inness_research.The city is a complex system that evolves through its inherent social and economic interactions. Mediating the movements of people and resources, urban street networks offer a spatial footprint of these activities. Of particular interest is the interplay between street structure and its functional usage. Here, we study the shape of 472,040 spatiotemporally optimized travel routes in the 92 most populated cities in the world, finding that their collective morphology exhibits a directional bias influenced by the attractive (or repulsive) forces resulting from congestion, accessibility, and travel demand. To capture this, we develop a simple geometric measure, inness, that maps this force field. In particular, cities with common inness patterns cluster together in groups that are correlated with their putative stage of urban development as measured by a series of socio-economic and infrastructural indicators, suggesting a strong connection between urban development, increasing physical connectivity, and diversity of road hierarchies.US Army Research OfficeNational Research Foundation of Korea funded by the Ministry of Science and ICTMinistry of Education of the Republic of Kore
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
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