60 research outputs found
Assessing the significance of knockout cascades in metabolic networks
Complex networks have been shown to be robust against random structural
perturbations, but vulnerable against targeted attacks. Robustness analysis
usually simulates the removal of individual or sets of nodes, followed by the
assessment of the inflicted damage. For complex metabolic networks, it has been
suggested that evolutionary pressure may favor robustness against reaction
removal. However, the removal of a reaction and its impact on the network may
as well be interpreted as selective regulation of pathway activities,
suggesting a tradeoff between the efficiency of regulation and vulnerability.
Here, we employ a cascading failure algorithm to simulate the removal of single
and pairs of reactions from the metabolic networks of two organisms, and
estimate the significance of the results using two different null models:
degree preserving and mass-balanced randomization. Our analysis suggests that
evolutionary pressure promotes larger cascades of non-viable reactions, and
thus favors the ability of efficient metabolic regulation at the expense of
robustness
How to Compare the Scientific Contributions between Research Groups
We present a method to analyse the scientific contributions between research
groups. Given multiple research groups, we construct their journal/proceeding
graphs and then compute the similarity/gap between them using network analysis.
This analysis can be used for measuring similarity/gap of the topics/qualities
between research groups' scientific contributions. We demonstrate the
practicality of our method by comparing the scientific contributions by Korean
researchers with those by the global researchers for information security in
2006 - 2008. The empirical analysis shows that the current security research in
South Korea has been isolated from the global research trend
Network harness: bundles of routes in public transport networks
Public transport routes sharing the same grid of streets and tracks are often
found to proceed in parallel along shorter or longer sequences of stations.
Similar phenomena are observed in other networks built with space consuming
links such as cables, vessels, pipes, neurons, etc. In the case of public
transport networks (PTNs) this behavior may be easily worked out on the basis
of sequences of stations serviced by each route. To quantify this behavior we
use the recently introduced notion of network harness. It is described by the
harness distribution P(r,s): the number of sequences of s consecutive stations
that are serviced by r parallel routes. For certain PTNs that we have analyzed
we observe that the harness distribution may be described by power laws. These
power laws observed indicate a certain level of organization and planning which
may be driven by the need to minimize the costs of infrastructure and secondly
by the fact that points of interest tend to be clustered in certain locations
of a city. This effect may be seen as a result of the strong interdependence of
the evolutions of both the city and its PTN.
To further investigate the significance of the empirical results we have
studied one- and two-dimensional models of randomly placed routes modeled by
different types of walks. While in one dimension an analytic treatment was
successful, the two dimensional case was studied by simulations showing that
the empirical results for real PTNs deviate significantly from those expected
for randomly placed routes.Comment: 12 pages, 24 figures, paper presented at the Conference ``Statistical
Physics: Modern Trends and Applications'' (23-25 June 2009, Lviv, Ukaine)
dedicated to the 100th anniversary of Mykola Bogolyubov (1909-1992
Tunable and Growing Network Generation Model with Community Structures
Recent years have seen a growing interest in the modeling and simulation of
social networks to understand several social phenomena. Two important classes
of networks, small world and scale free networks have gained a lot of research
interest. Another important characteristic of social networks is the presence
of community structures. Many social processes such as information diffusion
and disease epidemics depend on the presence of community structures making it
an important property for network generation models to be incorporated. In this
paper, we present a tunable and growing network generation model with small
world and scale free properties as well as the presence of community
structures. The major contribution of this model is that the communities thus
created satisfy three important structural properties: connectivity within each
community follows power-law, communities have high clustering coefficient and
hierarchical community structures are present in the networks generated using
the proposed model. Furthermore, the model is highly robust and capable of
producing networks with a number of different topological characteristics
varying clustering coefficient and inter-cluster edges. Our simulation results
show that the model produces small world and scale free networks along with the
presence of communities depicting real world societies and social networks.Comment: Social Computing and Its Applications, SCA 13, Karlsruhe : Germany
(2013
Eigenvector-based identification of bipartite subgraphs
We report our experiments in identifying large bipartite subgraphs of simple
connected graphs which are based on the sign pattern of eigenvectors belonging
to the extremal eigenvalues of different graph matrices: adjacency, signless
Laplacian, Laplacian, and normalized Laplacian matrix. We compare the
performance of these methods to a local switching algorithm based on the Erdos
bound that each graph contains a bipartite subgraph with at least half of its
edges. Experiments with one scale-free and three random graph models, which
cover a wide range of real-world networks, show that the methods based on the
eigenvectors of the normalized Laplacian and the adjacency matrix yield
slightly better, but comparable results to the local switching algorithm. We
also formulate two edge bipartivity indices based on the former eigenvectors,
and observe that the method of iterative removal of edges with maximum
bipartivity index until one obtains a bipartite subgraph, yields comparable
results to the local switching algorithm, and significantly better results than
an analogous method that employs the edge bipartivity index of Estrada and
Gomez-Gardenes.Comment: 20 pages, 8 figure
Stay Awhile and Listen: User Interactions in a Crowdsourced Platform Offering Emotional Support
Internet and online-based social systems are rising as the dominant mode of
communication in society. However, the public or semi-private environment under
which most online communications operate under do not make them suitable
channels for speaking with others about personal or emotional problems. This
has led to the emergence of online platforms for emotional support offering
free, anonymous, and confidential conversations with live listeners. Yet very
little is known about the way these platforms are utilized, and if their
features and design foster strong user engagement. This paper explores the
utilization and the interaction features of hundreds of thousands of users on 7
Cups of Tea, a leading online platform offering online emotional support. It
dissects the level of activity of hundreds of thousands of users, the patterns
by which they engage in conversation with each other, and uses machine learning
methods to find factors promoting engagement. The study may be the first to
measure activities and interactions in a large-scale online social system that
fosters peer-to-peer emotional support
Evolving Clustered Random Networks
We propose a Markov chain simulation method to generate simple connected
random graphs with a specified degree sequence and level of clustering. The
networks generated by our algorithm are random in all other respects and can
thus serve as generic models for studying the impacts of degree distributions
and clustering on dynamical processes as well as null models for detecting
other structural properties in empirical networks
Contagions in Random Networks with Overlapping Communities
We consider a threshold epidemic model on a clustered random graph with
overlapping communities. In other words, our epidemic model is such that an
individual becomes infected as soon as the proportion of her infected neighbors
exceeds the threshold q of the epidemic. In our random graph model, each
individual can belong to several communities. The distributions for the
community sizes and the number of communities an individual belongs to are
arbitrary.
We consider the case where the epidemic starts from a single individual, and
we prove a phase transition (when the parameter q of the model varies) for the
appearance of a cascade, i.e. when the epidemic can be propagated to an
infinite part of the population. More precisely, we show that our epidemic is
entirely described by a multi-type (and alternating) branching process, and
then we apply Sevastyanov's theorem about the phase transition of multi-type
Galton-Watson branching processes. In addition, we compute the entries of the
matrix whose largest eigenvalue gives the phase transition.Comment: Minor modifications for the second version: added comments (end of
Section 3.2, beginning of Section 5.3); moved remark (end of Section 3.1,
beginning of Section 4.1); corrected typos; changed titl
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