55 research outputs found
The Blogosphere at a Glance â Content-Based Structures Made Simple
A network representation based on a basic wordoverlap
similarity measure between blogs is introduced.
The simplicity of the representation renders
it computationally tractable, transparent and insensitive
to representation-dependent artifacts. Using
Swedish blog data, we demonstrate that the representation,
in spite of its simplicity, manages to capture
important structural properties of the content
in the blogosphere. First, blogs that treat similar
subjects are organized in distinct network clusters.
Second, the network is hierarchically organized as
clusters in turn form higher-order clusters: a compound
structure reminiscent of a blog taxonomy
Autonomous Accident Monitoring Using Cellular Network Data
Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions
A method for inferring hierarchical dynamics in stochastic processes
Complex systems may often be characterized by their hierarchical dynamics. In
this paper do we present a method and an operational algorithm that
automatically infer this property in a broad range of systems; discrete
stochastic processes. The main idea is to systematically explore the set of
projections from the state space of a process to smaller state spaces, and to
determine which of the projections that impose Markovian dynamics on the
coarser level. These projections, which we call Markov projections, then
constitute the hierarchical dynamics of the system. The algorithm operates on
time series or other statistics, so a priori knowledge of the intrinsic
workings of a system is not required in order to determine its hierarchical
dynamics. We illustrate the method by applying it to two simple processes; a
finite state automaton and an iterated map.Comment: 16 pages, 12 figure
Quasi-Species and Aggregate Dynamics
At an early stage in pre-biotic evolution, groups of replicating molecules
must coordinate their reproduction to form aggregated units of selection.
Mechanisms that enable this to occur are currently not well understood. In this
paper we introduce a deterministic model of primitive replicating aggregates,
proto-organisms, that host populations of replicating information carrying
molecules. Some of the molecules promote the reproduction of the proto-organism
at the cost of their individual replication rate. A situation resembling that
of group selection arises. We derive and analytically solve a partial
differential equation that describes the system. We find that the relative
prevalence of fast and slow replicators is determined by the relative strength
of selection at the aggregate level to the selection strength at the molecular
level. The analysis is concluded by a preliminary treatment of finite
population size effects.Comment: 6 page
Complexity of Networks (reprise)
Network or graph structures are ubiquitous in the study of complex systems.
Often, we are interested in complexity trends of these system as it evolves
under some dynamic. An example might be looking at the complexity of a food web
as species enter an ecosystem via migration or speciation, and leave via
extinction.
In a previous paper, a complexity measure of networks was proposed based on
the {\em complexity is information content} paradigm. To apply this paradigm to
any object, one must fix two things: a representation language, in which
strings of symbols from some alphabet describe, or stand for the objects being
considered; and a means of determining when two such descriptions refer to the
same object. With these two things set, the information content of an object
can be computed in principle from the number of equivalent descriptions
describing a particular object.
The previously proposed representation language had the deficiency that the
fully connected and empty networks were the most complex for a given number of
nodes. A variation of this measure, called zcomplexity, applied a compression
algorithm to the resulting bitstring representation, to solve this problem.
Unfortunately, zcomplexity proved too computationally expensive to be
practical.
In this paper, I propose a new representation language that encodes the
number of links along with the number of nodes and a representation of the
linklist. This, like zcomplexity, exhibits minimal complexity for fully
connected and empty networks, but is as tractable as the original measure.
...Comment: Accepted in Complexit
Competing associations in six-species predator-prey models
We study a set of six-species ecological models where each species has two
predators and two preys. On a square lattice the time evolution is governed by
iterated invasions between the neighboring predator-prey pairs chosen at random
and by a site exchange with a probability Xs between the neutral pairs. These
models involve the possibility of spontaneous formation of different defensive
alliances whose members protect each other from the external invaders. The
Monte Carlo simulations show a surprisingly rich variety of the stable spatial
distributions of species and subsequent phase transitions when tuning the
control parameter Xs. These very simple models are able to demonstrate that the
competition between these associations influences their composition. Sometimes
the dominant association is developed via a domain growth. In other cases
larger and larger invasion processes preceed the prevalence of one of the
stable asociations. Under some conditions the survival of all the species can
be maintained by the cyclic dominance occuring between these associations.Comment: 8 pages, 9 figure
Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales
Concepts used in the scientific study of complex systems have become so
widespread that their use and abuse has led to ambiguity and confusion in their
meaning. In this paper we use information theory to provide abstract and
concise measures of complexity, emergence, self-organization, and homeostasis.
The purpose is to clarify the meaning of these concepts with the aid of the
proposed formal measures. In a simplified version of the measures (focusing on
the information produced by a system), emergence becomes the opposite of
self-organization, while complexity represents their balance. Homeostasis can
be seen as a measure of the stability of the system. We use computational
experiments on random Boolean networks and elementary cellular automata to
illustrate our measures at multiple scales.Comment: 42 pages, 11 figures, 2 table
A model-independent approach to infer hierarchical codon substitution dynamics
<p>Abstract</p> <p>Background</p> <p>Codon substitution constitutes a fundamental process in molecular biology that has been studied extensively. However, prior studies rely on various assumptions, e.g. regarding the relevance of specific biochemical properties, or on conservation criteria for defining substitution groups. Ideally, one would instead like to analyze the substitution process in terms of raw dynamics, independently of underlying system specifics. In this paper we propose a method for doing this by identifying groups of codons and amino acids such that these groups imply closed dynamics. The approach relies on recently developed spectral and agglomerative techniques for identifying hierarchical organization in dynamical systems.</p> <p>Results</p> <p>We have applied the techniques on an empirically derived Markov model of the codon substitution process that is provided in the literature. Without system specific knowledge of the substitution process, the techniques manage to "blindly" identify multiple levels of dynamics; from amino acid substitutions (via the standard genetic code) to higher order dynamics on the level of amino acid groups. We hypothesize that the acquired groups reflect earlier versions of the genetic code.</p> <p>Conclusions</p> <p>The results demonstrate the applicability of the techniques. Due to their generality, we believe that they can be used to coarse grain and identify hierarchical organization in a broad range of other biological systems and processes, such as protein interaction networks, genetic regulatory networks and food webs.</p
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